Publikationen

Zusammenfassung

Small Delay Faults (SDFs) introduce additional delays smaller than the capture time and require timing-aware test pattern generation. Since timing variations can invalidate the effectiveness of such patterns, different circuit instances may show a different fault coverage for the same test pattern set. This paper presents a method to generate test pattern sets for SDFs which are valid for all circuit timings introduced by process and temperature-induced variations. The method overcomes the limitations of known timing-aware Automatic Test Pattern Generation (ATPG) which has to use fault sampling under process variations due to the computational complexity. A statistical learning scheme maximises the coverage of SDFs in circuits following the variation parameters of a calibrated in- dustrial FinFET transistor model. The method combines efficient ATPG for Transition Faults (TFs) with fast timing-aware fault simulation on GPUs. Simulation experiments show that the size of the pattern set is significantly reduced in comparison to standard N-detection for transition faults, while the fault coverage even increases.

Zusammenfassung

Effective error-resilient communication is crucial, especially in autonomous systems. However, some applications can accept a certain level of approximation within a defined range. This opens up opportunities for optimizing system objectives, like minimizing error rates, extending operational lifetimes or reduc- ing power consumption. In this study, we introduce an innovative approach called RAPPER (Robust and Approximate Error Toler- ating Communication) which combines approximation and error correcting codes for optimizing the transmitted data. The under- lying communication scheme exploits an extension of differential encoding to enhance communication performance. Simulation results show an increased interconnect mission time and perfor- mance, while simultaneously achieving significant reductions in error rates and power consumption.

Zusammenfassung

Logic Built-In Self-Test (LBIST) with stored de- terministic patterns is supported by the major CAD vendors and is gaining increasing attention, especially for safety-critical applications such as automotive. It is used for both manufacturing and periodic in-field testing. An unresolved challenge so far stems from the inevitable process. This paper presents the first approach for storage-based BIST addressing delay faults under process variations and multiple voltages. A unified solution for pattern generation, test set compaction and BIST hardware is presented that is compatible with commercial schemes. The solu- tion significantly outperforms traditional N-detect for transition faults in terms of test set size, test application time and fault efficiency.

Zusammenfassung

It has been known and explored for many years that low voltage testing amplifies the effect of a defect, increasing the size of a Small Delay Fault (SDF) and, in the best case, turning SDFs into Stuck-at-faults. It is often overlooked that Vmin testing poses an additional challenge to the test pattern generation method under process variations. The standard deviation of gate delays under Vmin is a multiple of that under nominal voltage. The increased variation will invalidate the efficiency of test patterns generated under nominal voltage and significantly reduce fault coverage. This paper presents the first test pattern generation algorithm specifically tuned for Vmin testing which obtains higher fault coverage by smaller test sets than those generated for nominal voltage.

Zusammenfassung

Many applications allow errors during communication as long as their sizes or rates are limited. These applications are perfect candidates for approximate communication and provide sufficient degrees of freedom to optimize the communicated data for performance, power, reliability and safety within certain error bounds. In this study, we present a novel approach for approximate communication which is specifically tailored to the streaming of data between sensors and control units on narrow parallel buses. To meet the performance goals, the presented approach relies on base-delta compression, where a base value is followed by a sequence of differences to it (deltas). To ensure an efficient low power transmission of data as well as a high reliability and safety at the same time, the approach uses a Gray code for bus encoding and approximates the data within given error bounds, such that crosstalk induced faults and currents are minimized and the overall switching activity is kept low.

Zusammenfassung

Small Delay Faults (SDFs) introduce additional delays smaller than the capture time and require timing-aware test pattern generation. Since process variations can invalidate the effectivity of such patterns, different circuit instances may show a different fault coverage. This paper presents a method to generate test pattern sets for SDFs which are valid for all circuit timings. The method overcomes the complexity limitations of known timing-aware Automatic Test Pattern Generation (ATPG) which has to use fault sampling under process variations. A statistical learning scheme maximises the coverage of SDFs in an entire circuit population following the variation parameters of a calibrated industrial FinFET transistor model. The method combines efficient ATPG for Transition Faults with fast timing-aware fault simulation on GPUs. Experiments show that the size of the pattern set is significantly reduced in comparison to standard N-detect while the fault coverage even increases compared to both N-detect and timing-aware ATPG

Zusammenfassung

The Preconditioned Conjugate Gradient (PCG) method is well-established for solving linear equations systems. Running the PCG method on a hardware accelerator ensures fast and efficient computation. At the same time, each hardware accelerator may be slightly different due to process variability or aging. To handle the variability, a rather pessimistic frequency selection for the whole population of accelerators is often utilized. Increasing the frequency may improve the performance but may also increase the risk of computational errors, affect the convergence of PCG or even corrupt the PCG results. In this paper, we present a method to determine the frequency for each hardware accelerator instance which optimizes the execution time and the energy efficiency of the PCG method. First, a technique is presented to analyze the error resilience of a PCG algorithm to overclocking. Based on the analysis results, we increase the frequency to speed up the convergence while keeping the error rate below the required threshold.

Zusammenfassung

Many applications from Artificial Intelligence (AI) and Scientific Computing rely on efficient algorithms for solving large systems of linear equations. The Preconditioned Conjugate Gradient (PCG) algorithm is a promising option and it is a perfect candidate to be executed on specialized hardware accelerators widely used in AI. Hardware accelerators, like other modern devices, are prone to process variations. A conventional approach to handle the variability is to use pessimistic guard�bands for all the devices within the population, which implies that the best and even the average accelerators are slowed down significantly. Since the PCG algorithm is inherently error resilient to some extent, it may also tolerate an error rate increase due to overclocking. On another side, increasing the frequency may increase the total execution time if more arithmetic operations are needed until the convergence. This paper presents a method to ensure efficient computing on each hardware accelerator instance running the PCG algorithm. A cross-layer approach identifies an optimized frequency that minimizes the total time to complete the PCG algorithm. Simple high-level checks ensure the quality of the solution. Experimental results validate the feasibility of the developed approach for large systems of linear equations.

Zusammenfassung

The high-volume manufacturing test ensures the production of defect-free devices, which is of utmost importance when dealing with safety-critical systems. Such a high-quality test requires a deliberately designed scan network to provide a time and cost-effective access to many on-chip components, as included in state-of-the-art chip designs. The IEEE 1687 Std. (IJTAG) has been introduced to tackle this challenge by adding programmable components that enables the design of reconfig- urable scan networks. Although these networks reduce the test time by shortening the scan chains’ lengths, the reconfiguration process itself incurs an additional time overhead. This paper proposes a heuristic method for designing customized multi-power domain reconfigurable scan networks with a minimized overall reconfiguration time. More precisely, the proposed method exploits a-priori given non-functional properties of the system, such as the power characteristics and the instruments’ access requirements. For the first time, these non-functional properties are considered to synthesize a well-adjusted and highly efficient multi-power domain network. The experimental results show a considerable improvement over the reported benchmark networks.

Zusammenfassung

This review provides a comprehensive examination of the state-of-the-art research on resistive switching (RS) in BiFeO(3) (BFO)-based memristive devices. By exploring possible fabrication techniques for preparing the functional BFO layers in memristive devices, the constructed lattice systems and corresponding crystal types responsible for RS behaviors in BFO-based memristive devices are analyzed. The physical mechanisms underlying RS in BFO-based memristive devices, i.e., ferroelectricity and valence change memory, are thoroughly reviewed, and the impact of various effects such as the doping effect, especially in the BFO layer, is evaluated. Finally, this review provides the applications of BFO devices and discusses the valid criteria for evaluating the energy consumption in RS and potential optimization techniques for memristive devices.

Zusammenfassung

System health monitoring is an integral concept that involves observing, evaluating, and adapting the system behavior under varying operating conditions. The data can be collected from embedded instruments throughout the lifetime. Various techniques, including machine learning, have to be used to analyze the data and adapt the underlying system behavior. At the same time, the behavior of modern devices is affected by different types of variations. In order to develop an efficient and precise health monitoring scheme, the underlying analysis and adaptation techniques must be robust even in the presence of those variations. This contribution explores various strategies for overcoming this challenge across the system stack.

Zusammenfassung

Reconfigurable Scan Networks (RSNs) are widely used for accessing instruments offline during debug, test and validation, as well as for performing sys-tem-level-test and online system health monitoring. The correct operation of RSNs is essential, and RSNs have to be thoroughly tested. However, due to their inherently sequential structure and complex control dependencies, large parts of RSNs have limited observability and controllability. As a result, certain faults at the interfaces to the instruments, control primitives and scan segments remain undetected by existing test methods. In the paper at hand, Design-for-test (DfT) schemes are developed to overcome the testability problems e.g. by resynthesizing the initial design. A DfT scheme for RSNs is presented, which allows detecting all single stuck-at-faults in RSNs by using existing test generation techniques. The developed scheme analyzes and ensures the testability of all parts of RSNs, which include scan segments, control primitives, and interfaces to the instruments. Therefore, the developed scheme is referred to as a complete DfT scheme. It allows for a test integration to cover multiple fault locations can with a single efficient test sequence and to reduce overall test cost.

Zusammenfassung

Small Delay Faults (SDFs) due to weak defects and marginalities have to be distinguished from extra delays due to process variations, since they may form a reliability threat even if the resulting timing is within the specification. In this paper, it is shown that these faults can still be identified, even if the corresponding defect cell is deeply embedded into a combinational circuit and its observability is restricted. The results of a few delay tests at different voltages and frequencies serve as the input to machine learning procedures which can classify a circuit as marginal due to defects or just slow due to variations. Several machine learning techniques are investigated and compared with respect to accuracy, precision, and recall for different circuit sizes and defect scales. The classification strategies are powerful enough to sort out defective devices without a major impact on yield.

Zusammenfassung

Innovative manufacturing processes allow the integration of billions of transistors into a single chip, and the implementation of extremely dense designs, but they also come with increased variability and new reliability threats. Both, new processes and more complex designs introduce a higher degree of indeterminism which challenges logic diagnosis and defect classification. Design techniques like selective hardening, fault masking, and error masking or adaptive voltage and frequency scaling (AVFS) increase reliability, but make test, diagnosis, and defect classification even more difficult. This chapter presents machine learning techniques for distinguishing variations from reliability threats due to defects, for identifying different defect types during diagnosis, and for distinguishing transient errors covered by hardening or masking from intermittent faults.

Zusammenfassung

We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list of assets, the portfolio optimization problem is formulated as quadratic binary optimization constrained on the number of assets contained in the portfolio. QAOA has been suggested as a possible candidate for solving this problem (and similar combinatorial optimization problems) more efficiently than classical computers in the case of a sufficiently large number of assets. However, the practical implementation of this algorithm requires a careful consideration of several technical issues, not all of which are discussed in the present literature. The present article intends to fill this gap and thereby provides the reader with a useful guide for applying QAOA to the portfolio optimization problem (and similar problems). In particular, we will discuss several possible choices of the variational form and of different classical algorithms for finding the corresponding optimized parameters. Viewing at the application of QAOA on error-prone NISQ hardware, we also analyse the influence of statistical sampling errors (due to a finite number of shots) and gate and readout errors (due to imperfect quantum hardware). Finally, we define a criterion for distinguishing between `easy' and `hard' instances of the portfolio optimization problem.

Zusammenfassung

With the advent of the Internet of Things, nanoelectronic devices or memristors have been the subject of significant interest for use as new hardware security primitives. Among the several available memristors, BiFe\$\$\backslashmathrmØ\\_\3\\$\$ (BFO)-based electroforming-free memristors have attracted considerable attention due to their excellent properties, such as long retention time, self-rectification, intrinsic stochasticity, and fast switching. They have been actively investigated for use in physical unclonable function (PUF) key storage modules, artificial synapses in neural networks, nonvolatile resistive switches, and reconfigurable logic applications. In this work, we present a physics-inspired 1D compact model of a BFO memristor to understand its implementation for such applications (mainly PUFs) and perform circuit simulations. The resistive switching based on electric field-driven vacancy migration and intrinsic stochastic behaviour of the BFO memristor are modelled using the cloud-in-a-cell scheme. The experimental current--voltage characteristics of the BFO memristor are successfully reproduced. The response of the BFO memristor to changes in electrical properties, environmental properties (such as temperature) and stress are analyzed and consistant with experimental results.

Zusammenfassung

Reconfigurable Scan Networks (RSNs) access embedded instruments throughout the whole system lifecycle. To support dependability management by means of RSNs, RSNs themselves must be continuously tested. The paper-at-hand presents the first online periodic test method for RSNs. The developed algorithm generates a short sequence of test patterns, which tests all parts of an RSN. The generated sequence is uploaded on-chip and is applied periodically to avoid fault accumulation in RSNs. The overall test application time is minimized to comply with the timing requirements of the well-known safety standards. The experimental results show that the method is efficient for all considered RSN designs and is scalable with the increasing size and complexity of RSNs.

Zusammenfassung

Both process variations and defects in cells can lead to additional small delays within specifications, while the latter must be identified because they may degrade soon into critical faults for circuits and result in threat to reliability. Therefore, discriminating small delays due to defects from those due to variations has drawn increasingly attention in the test community over the recent years. One promising research direction is to formulate the task into binary classification by using delays under a few supply voltages as the only variables for datadriven algorithms. However, many approaches often assume the availability of delay information from both defective and nondefective cells or combinational circuits. This assumption implies a large time consumption for simulation, and considerable costs for manufactured defective devices. To address the issues above, this paper proposes to use unsupervised deep learning technique to train an recognizer on non-defective data only but still can identify defects during inference. Specifically, we have proposed weighted autoencoder with a novel data augmentation technique to solve this problem. Experiments show that our approach has comparable detection capability as supervised learning schemes, while our method does not require any defective data. Moreover, our approach is more robust to unbalanced datasets and to nontarget defects than other methods.

Zusammenfassung

Adaptive Voltage Frequency Scaling (AVFS) is an important means to overcome process-induced variability challenges for advanced high-performance circuits. AVFS requires and allows determining the maximum speed Fmax(Vdd) reachable under a set of certain operation voltages Vdd. In this paper, it is shown that the Fmax(Vdd) measurements contain relevant data to identify some hidden defects in a chip which are reliability threats and can cause device failures, but pass the speed binning procedure within the given specifications. Static Timing Analysis (STA) is applied to a circuit designed by using standard cell libraries in which the underlying transistors along with process variations have been carefully calibrated against industrial 14nm FinFET measurement data, and instances with and without injected small resistive open defects are generated. From the slope of the function Fmax(Vdd), a machine learning procedure can identify some defects with high accuracy and few false positives. These chips can be then discarded without any further need and cost for testing. It has to be noted that this reliability information comes for free from the data which is already generated, and does not need any additional measurements.

Zusammenfassung

Reconfigurable Scan Networks (RSNs) access the evaluation results from embedded instruments and control their operation throughout the device lifetime. At the same time, a single fault in an RSN may dramatically reduce the accessibility of the instruments. During post-silicon validation, it may prevent extracting the complete data from a device. During online operation, the inaccessibility of runtime-critical instruments via a defect RSN may eventually result in a system failure. This paper addresses both scenarios above by presenting robust RSNs. We show that by making a small number of carefully selected spots in RSNs more robust, the entire access mechanism becomes significantly more reliable. A flexible cost function assesses the importance of specific control primitives for the overall accessibility of the instruments. Following the cost function, a minimized number of spots is hardened against permanent faults. All the critical instruments as well as most of the remaining instruments remain accessible through the resulting RSNs even in the presence of defects. In contrast to state-of-the-art fault-tolerant RSNs, the presented approach does not change the RSN topology and needs less hardware overhead. Selective hardening is formulated as a multi-objective optimization problem and solved by using an evolutionary algorithm. The experimental results validate the efficiency and the scalability of the approach.

Zusammenfassung

Safety-critical systems have to follow extremely high dependability requirements as specified in the standards for automotive, air, and space applications. The required high fault coverage at runtime is usually obtained by a combination of concurrent error detection or correction and periodic tests within rather short time intervals. The concurrent scheme ensures the integrity of computed results while the periodic test has to identify potential aging problems and to prevent any fault accumulation which may invalidate the concurrent error detection mechanism. Such periodic built-in self-test (BIST) schemes are already commercialized for memories and for ran- dom logic. The paper at hand extends this approach to inter- connect structures. A BIST scheme is presented which targets interconnect defects before they will actually affect the system functionality at nominal speed. A BIST schedule is developed which significantly reduces aging caused by electromigration during the lifetime application of the periodic test.

Zusammenfassung

Reconfigurable Scan Networks (RSNs) enable an efficient reliability management throughout the device lifetime. They can be used for controlling integrated instruments, such as aging monitors or built-in self-test (BIST) registers, as well as for collecting the evaluation results from them. At the same time, they may impose a security threat, since the additional connectivities introduced by the RSN can possibly be misused as a side-channel. This paper presents an approach for Security Compliance Analysis and Resynthesis (SCAR) of RSNs to integrate an RSN compliant with the security properties of the initial design. First, the reachability properties of the original design are accurately computed. The connectivities inside the RSN, which exceed the allowed connectivity of the initial design, are identified using the presented Security Compliance Analysis. Next, all violations are resolved by automated Resynthesis with a minimized number of structural changes. As a result of SCAR, any information leakage due to the RSN integration is prevented, while the accessibility of the instruments through the RSN is preserved. The approach is able to analyze complex control dependencies and obtains a compliant RSN even for the largest available benchmarks.

Zusammenfassung

Light field (LF) imaging, which captures both spatial and angular information of a scene, is undoubtedly beneficial to numerous applications. Although various techniques have been proposed for LF acquisition, achieving both angularly and spatially high-resolution LF remains a technology challenge. In this paper, a learning-based approach applied to 3D epipolar image (EPI) is proposed to reconstruct high-resolution LF. Through a 2-stage super-resolution framework, the proposed approach effectively addresses various LF super-resolution (SR) problems, i.e., spatial SR, angular SR, and angular-spatial SR. While the first stage provides flexible options to up-sample EPI volume to the desired resolution, the second stage, which consists of a novel EPI volume-based refinement network (EVRN), substantially enhances the quality of the high-resolution EPI volume. An extensive evaluation on 90 challenging synthetic and real-world light field scenes from 7 published datasets shows that the proposed approach outperforms state-of-the-art methods to a large extend for both spatial and angular super-resolution problem, i.e., an average peak signal to noise ratio improvement of more than 2.0 dB, 1.4 dB, and 3.14 dB in spatial SR ×2, spatial SR ×4, and angular SR respectively. The reconstructed 4D light field demonstrates a balanced performance distribution across all perspective images and presents superior visual quality compared to the previous works.

Zusammenfassung

As cyber-attacks grow in frequency and complexity every year, digital systems critically rely on complex security mechanisms. With Approximate Computing (AxC) reaching maturity for practical application, the long-neglected security implications of this paradigm must be understood and addressed. This chapter provides an overview of both: security threats potentially stemming from AxC hardware blocks, and opportunities for better security constructions based on AxC. Concerning the threats, we will discuss security issues related with design and manufacturing (reverse engineering, hardware Trojans, counterfeiting, and piracy) of approximate circuits and with their operation (side channel, covert-channel, and fault-injection attacks). For each threat, we will discuss the consequences of the transition from classical to approximate circuits. We will then present two promising applications of AxC technology in homomorphic encryption and in defending neural networks against adversarial attacks. Approximated homomorphic encryption can reduce the computational overhead of large-scale computations performed in the encrypted domain. We will provide an implementation angle for scenarios such as cloud computing or machine learning/AI, where the improved efficiency of the computation is particularly beneficial. Then, we will discuss how stochastic computing (a type of AxC) implementations of neural networks can be made more resilient against adversarial attacks while virtually retaining their classification accuracy.

Zusammenfassung

Small Delay Faults (SDFs) due to defects and marginalities have to be distinguished from extra delays due to process variations, since they may form a reliability threat even if the resulting timing is in the specification. In this paper, it is shown that these faults can still be identified, even if the corresponding defect cell is deeply embedded into a combinational circuit and its behavioral features are affected by several masking impacts of the rest of the circuit. The results of a few delay tests at different voltages and frequencies serve as the input to machine learning procedures which can classify a circuit as marginal due to defects or just slow due to variations. Several machine learning techniques are investigated and compared with respect to accuracy, precision, and recall for different circuit sizes and defect scales. The classification strategies are powerful enough to sort out defect devices without a major impact on yield.

Zusammenfassung

Reconfigurable Scan Networks (RSNs) have to be tested before they can be used for post-silicon validation, diagnosis or online reliability management. Even a single stuck-at fault in the switch logic of an RSN can corrupt the scan paths and make instruments inaccessible. Testing the switch logic of an RSN is a complex sequential test problem. The existing test schemes for RSNs rely on the assumption that a fault in the switch logic will be detected by the altered length of the erroneously activated scan path. However, often this assumption does not hold and faults in the switch logic remain undetected. In this paper, an automated testability-enhancing resynthesis is presented. First, the testability of the initial RSN is accurately analyzed. If any single fault in the switch logic is undetectable by the altered path length, a small number of scan cells is inserted into the RSN. The presented scheme is applicable to arbitrary RSN designs and is compliant with state-of-the-art test methods and the applicable standards. The experimental results show the efficacy, the efficiency and the scalability of the approach.

Zusammenfassung

Self-aware and safety-critical hardware/software systems rely on a variety of embedded instruments, sensors, monitors and design-for-test circuitry to check the system integrity. The access to these internal instruments is supported by standards commonly called iJTAG and employs so called reconfigurable scan networks (RSNs), which are more and more used at runtime, too. They collect periodically and also concurrently the information on the circuit’s health state and deliver it to some dependability management unit. The integrity of RSNs is essential for the dependability of selfaware systems and can be ensured by a combination of periodic and concurrent test methods of the RSN itself. The paper at hand presents the first concurrent online test method for RSNs by adding a brief integrity test to each access operation. The presented scheme includes a hardware extension of negligible size, supports offline test, diagnosis and post-silicon validation as well, and is further referred as ROSTI : RSN Online/Offline SelfTest Infrastructure. It exploits the original RSN control signals and does not require any modification of the underlying RSN. The hardware costs are independent of the size of the RSN, and ROSTI is flexible for generating different test sequences for different types of faults. The experimental results validate these characteristics and show that ROSTI is highly scalable.

Zusammenfassung

While scalable, fully error corrected quantum computing is years or even decades away, there is considerable interest in noisy intermediate-scale quantum computing (NISQ). In this paper, we introduce the ArsoNISQ framework that determines the tolerable error rate of a given quantum algorithm computation, i.e. quantum circuits, and the success probability of the computation given a success criterion and a NISQ computer. ArsoNISQ is based on simulations of quantum circuits subject to errors according to the Pauli error model. ArsoNISQ was evaluated on a set of quantum algorithms that can incur a quantum speedup or are otherwise relevant to NISQ computing. Despite optimistic expectations in recent literature, we did not observe quantum algorithms with intrinsic robustness, i.e. algorithms that tolerate one error on average, in this evaluation. The evaluation demonstrated, however, that the quantum circuit size sets an upper bound for its tolerable error rate and quantified the difference in tolerate error rates for quantum circuits of similar sizes. Thus, the framework can assist quantum algorithm developers in improving their implementation and selecting a suitable NISQ computing platform. Extrapolating the results into the quantum advantage regime suggests that the error rate of larger quantum computers must decrease substantially or active quantum error correction will need to be deployed for most of the evaluated algorithms

Zusammenfassung

First quantum computers very recently have demonstrated “quantum supremacy” or “quantum advantage”: Executing a computation that would have been impossible on a classical machine. Today’s quantum computers follow the NISQ paradigm: They exhibit error rates that are much higher than in conventional electronics and have insufficient quantum resources to support powerful error correction protocols. This raises questions which relevant computations are within the reach of NISQ architectures. Several “NISQ-era algorithms” are assumed to match the specifics of such computers; for instance, variational optimisers are based on intertwining relatively short quantum and classical computations, thus maximizing the chances of success. This paper will critically assess the promise and challenge of NISQ computing. What has this field achieved so far, what are we likely to achieve soon, where do we have to be skeptical and wait for the advent of larger-scale fully error-corrected architectures?

Zusammenfassung

The reliable operation of integrated systems issupported by Reconfigurable Scan Networks (RSNs) which allow to access efficiently the embedded instruments throughout the life-cycle. However, the RSN integration may introduce additional connectivities into a Device-under-Test (DUT), and the RSN might be misused for information leakage. Structural methods resynthesize the RSNs and add hardware components such that certain instruments are physically separated, while functional approaches add filters to prevent certain access patterns. Both methods have certain limitations. This paper presents an effective approach to maximize the benefits and to overcome the limitations of the existing solutions by a hybrid combination of structural and functional protection schemes. A minimized number of structural changes is identified in order to resolve violations which cannot be handled by using sequence filters. The remaining violations are resolved functionally by using filters and a flexible protection can be enabled for multiple user groups with different access permissions. Since the majority of the violations are resolved using a filter, the hardware overhead for structural changes is drastically reduced. The efficiency of the approach is supported by experimental results.

Zusammenfassung

Runtime/reconfigurable architectures based on Field-Programmable Gate Arrays (FPGAs) are a promising augment to conventional processor architectures such as Central Processing Units (CPUs) and Graphic Processing Units (GPUs). Since the reconfigurable parts are typically manufactured in the latest technology, they may suffer from aging and environmentally induced dependability threats. In this chapter, strategic online test methods for dependable runtime-reconfigurable architectures as well as cross-layer optimizations for high reliability and lifetime are developed. Firstly, two orthogonal online tests are proposed that ensure reliable configuration of the reconfigurable fabric and aid fault detection. Secondly, a novel design method called module diversification is presented that enables self-repair of the system in case of faults caused by degradation effects as well as single-event upsets in the configuration. Thirdly, a novel stress-aware placement method is proposed that aims for slowing down system degradation by aging effects. The combined methods ensure reliable operation across architectural and gate level and allow to prolong the lifetime of dependable runtime-reconfigurable architectures.

Zusammenfassung

Artificial intelligence (AI) algorithms achieve outstanding results in many application domains such as computer vision and natural language processing. The performance of AI models is the outcome of complex and costly model architecture design and training processes. Hence, it is paramount for model owners to protect their AI models from piracy – model cloning, illegitimate distribution and use. IP protection mechanisms have been applied to AI models, and in particular to deep neural networks, to verify the model ownership. State-of-the-art AI model ownership protection techniques have been surveyed. The pros and cons of AI model ownership protection have been reported. The majority of previous works are focused on watermarking, while more advanced methods such fingerprinting and attestation are promising but not yet explored in depth. This study has been concluded by discussing possible research directions in the area.

Zusammenfassung

The preservation of cultural heritage assets of all kind is an important task for modern civilizations. This also includes tools and instruments that have been used in the previous decades and centuries. Along with the industrial revolution 200 years ago, mechanical and electrical technologies emerged, together with optical instruments. In the meantime, it is not only museums who showcase these developments, but also companies, universities, and private institutions. Gyroscopes are fascinating instruments with a history dating back 200 years. When J.G.F. Bohnenberger presented his machine to his students in 1810 at the University of Tuebingen, Germany, nobody could have foreseen that this fascinating development would be used for complex orientation and positioning. At the University of Stuttgart, Germany, a collection of 160 exhibits is available and in transition towards their sustainable future. Here, the systems are digitized in 2D, 2.5D, and 3D and are made available for a worldwide community using open access platforms. The technologies being used are computed tomography, computer vision, endoscopy, and photogrammetry. We present a novel workflow for combining voxel representations and colored point clouds, to create digital twins of the physical objects with 0.1 mm precision. This has not yet been investigated and is therefore pioneering work. Advantages and disadvantages are discussed and suggested work for the near future is outlined in this new and challenging field of tech heritage digitization.

Zusammenfassung

For investigations of concentration profiles around bubbles in millichannels as Taylor flow and in bubble columns two in situ real-time process analysis systems were developed. The first system uses laser Raman spectroscopy combined with real-time digital holography. The Raman part of the system is based on a custom pulsed high-energy laser. With this process analysis system, it is possible to measure concentrations of many chemical compounds selectively, with a spatial resolution in the micrometer range during a 10 µs laser pulse. Due to the two combined principles, the determination of the position of a measured local concentration relative to the gas bubble is possible and has been demonstrated. The second real-time process analysis system is especially suited for colored chemical reactions. The system is based on real-time UV/VIS 2D tomography such that with time the third dimension of a 3D concentration profile in the bubble wake can be determined. It consists of fast line sensors illuminated by a laser light sheet. This light sheet originates from a laser spot moving around one third of the bubble column repeatedly. This system is applicable to freely ascending single bubbles as well as bubbly flows in bubble columns.

Zusammenfassung

Purpose Deformable image registration is a fundamental task in medical imaging. Due to the large computational complexity of deformable registration of volumetric images, conventional iterative methods usually face the tradeoff between the registration accuracy and the computation time in practice. In order to boost the performance of deformable registration in both accuracy and runtime, we propose a fast unsupervised convolutional neural network for deformable image registration. Methods The proposed registration model FDRN possesses a compact encoder–decoder network architecture which employs a pair of fixed and moving images as input and outputs a three-dimensional displacement vector field (DVF) describing the offsets between the corresponding voxels in the fixed and moving images. In order to efficiently utilize the memory resources and enlarge the model capacity, we adopt additive forwarding instead of channel concatenation and deepen the network in each encoder and decoder stage. To facilitate the learning efficiency, we leverage skip connection within the encoder and decoder stages to enable residual learning and employ an auxiliary loss at the bottom layer with lowest resolution to involve deep supervision. Particularly, the low-resolution auxiliary loss is weighted by an exponentially decayed parameter during the training phase. In conjunction with the main loss in high-resolution grid, a coarse-to-fine learning strategy is achieved. Last but not least, we involve a proposed multi-label segmentation loss (SL) to improve the network performance in Dice score in case the segmentation prior is available. Comparing to the SL using average Dice score, the proposed SL does not require additional memory in the training phase and improves the registration accuracy efficiently. Results We evaluated FDRN on multiple brain MRI datasets from different aspects including registration accuracy, model generalizability, and model analysis. Experimental results demonstrate that FDRN performs better than the state-of-the-art registration method VoxelMorph by 1.46\% in Dice score in LPBA40. In addition to LPBA40, FDRN obtains the best Dice and NCC among all the investigated methods in the unseen MRI datasets including CUMC12, MGH10, ABIDE, and ADNI by a large margin. Conclusions The proposed FDRN provides better performance than the existing state-of-the-art registration methods for brain MR images by resorting to the compact autoencoder structure and efficient learning. Additionally, FDRN is a generalized framework for image registration which is not confined to a particular type of medical images or anatomy.

Zusammenfassung

Hidden delay defects (HDDs) are small delay defects that pass all at-speed tests at nominal capture time. They are an important indicator of latent defects that lead to early-life failures and aging problems that are serious especially in autonomous and medical applications. An effective way to screen out HDDs is to use Faster-than-At-Speed Testing (FAST) to observe outputs of sensitized non-critical paths which are expected to be stable earlier than nominal capture time.To improve the reliability of current and future designs, it is important to learn about the population of HDDs using logic diagnosis. We present the very first logic fault diagnosis technique that is able to identify HDDs by analyzing fail logs produced by FAST. Even with aggressive FAST testing, HDDs generate only very few failing test response bits. To overcome this severe challenge, we propose new backtracing and response matching methods that yield high diagnostic success rates even with very limited amount of failure data. The performance and scalability of our HDD diagnosis method is validated using fault injection campaigns with large benchmark circuits.

Zusammenfassung

Reliable operation, test, debug and diagnosis of complex integrated systems are ensured by em-bedded instruments, such as sensors, aging monitors or Built-In Self-test (BIST) registers. Reconfigurable Scan Networks (RSNs) offer a flexible and efficient way to access such test instruments throughout the whole life-cycle. However, improper RSN integration might introduce additional connectivity properties to the device under test (DUT), which can be exploited to perform unauthorized access or cause information leakage. The existence of such additional connectivity through the RSN can compromise the security of the DUT and is considered as a security threat.In this paper, a method is presented to resolve all such security compliance violations. The problem is formulated in terms of Integer Linear Programming (ILP) as a minimum cut problem in multicommodity flow. An efficient heuristic is presented, which, to our knowledge, for the first time allows to consider the whole set of violations simultaneously and thereby to find a minimized number of changes to the RSN structure in order to make it compliant with the initial security requirements of the DUT and prevent the information leakage through the scan chain.

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Stochastic computing (SC) is a promising computation technique for applications with huge numbers of individually simple operations. One of the main application targets for SC is the design of convolutional neural networks (CNNs) due to their heavy reliance on multiply-accumulate operations that have compact and power-efficient SC realizations. We present a training optimization method that can improve the accuracy of SC-based CNNs significantly. Using regularization techniques in combination with a newly developed retraining algorithm, we enforce sparsity, reduce the number of required MUX-based additions, and thus minimize undesired downscaling. Our results show that the proposed regularized training procedure (RTP) can reduce the computation time of SC-based NNs by a factor of four without sacrificing classification accuracy while also reducing area in hardware implementations. These findings suggest that SC can be successfully applied to more powerful CNNs than previously thought possible, thus extending the range of potential uses of SC.

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Hardware implementations of advanced cryptographic schemes gain importance for emerging cyber-physical and autonomous systems and with that the need for area-optimized hardware is also increasing. In this paper, we present a scalable, area-optimized elliptic curve ElGamal cryptosystem module that integrates both the encryption and decryption operations into a single block. With the help of the Universal Elliptic Curve Arithmetic unit, that is designed to perform elliptic curve point multiplication, doubling and addition, the area has been kept to a minimum to fit on the Xilinx Spartan-6 LX75 FPGA. The design supports the NIST-recommended curves over prime fields.

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Cryptographic hardware becomes increasingly vulnerable to physical attacks--both passive side-channel analysis and active fault injections--performed by skillful and well-equipped adversaries. In this paper, we introduce a technique that provides very high security against both types of attacks. It combines inner product masking (IPM), which offers higher-order side-channel attack resistance on word level and on bit level, with nonlinear security-oriented error-detection codes that provide robustness, i.e., strong detection guarantees for arbitrary faults. We prove that our scheme has the same security against side-channel attacks that an earlier, non-robust IPM-based solution has and in addition preserves robustness during addition and multiplication (and therefore arbitrary computations). Moreover, we prove that the information leakage from the checker is small and that the attack will be detected far before the attacker will gain significant information.

Zusammenfassung

Today’s electronic systems must simultaneously fulfill strict requirements on security and reliability. In particular, their cryptographic modules are exposed to faults, which can be due to natural failures (e.g., radiation or electromagnetic noise) or malicious fault-injection attacks. We present an architecture based on a new class of error-detecting codes that combine robustness properties with a minimal distance. The new architecture guarantees (with some probability) the detection of faults injected by an intelligent and strategic adversary who can precisely control the disturbance. At the same time it supports automatic correction of low-multiplicity faults. To this end, we discuss an efficient technique to correct single nibble/byte errors while avoiding full syndrome analysis. We also examine a Compact Protection Code (CPC)-based system level fault manager that considers this code an inner code (and the CPC as its outer code). We report experimental results obtained by physical fault injection on the SAKURA-G FPGA board. The experimental results reconfirm the assumption that faults may cause an arbitrary number of bit flips. They indicate that a combined inner–outer coding scheme can significantly reduce the number of fault events that go undetected due to erroneous corrections of the inner code.

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Systems with adaptive voltage-and frequency scaling (AVFS) require timing validation with accurate timing models under multiple operating points. Since, these models are typically simulated at logic level and extremely time-consuming, the state-of-the-art often compromises both accuracy and speed. This paper presents the first massively parallel switch level time simulator with parametric delay modeling for efficient timing-accurate validation of systems with AVFS. It provides full glitch-accurate switching activity information of designs under varying supply voltage and temperature. Offline statistical learning with regression analysis is employed to generate polynomials for dynamic delay modeling by approximation of the first-order electrical parameters of CMOS standard cells. With the parallelization on graphics processing units and simultaneous exploitation of multiple dimensions of parallelism the simulation throughput is maximized and scalable-design space exploration of AVFS-based systems is enabled. Results demonstrate the efficiency and accuracy with speedups of up to 159x over conventional logic level time simulation with static delays.

Zusammenfassung

Timing validation of systems with adaptive voltage-and frequency scaling (AVFS) requires an accurate timing model under multiple operating points. Simulating such a model at gate level is extremely time-consuming, and the state-of-the-art compromises both accuracy and compute efficiency.This paper presents a method for dynamic gate delay modeling on graphics processing unit (GPU) accelerators which is based on polynomial approximation with offline statistical learning using regression analysis. It provides glitch-accurate switching activity information for gates and designs under varying supply voltages with negligible memory and performance impact. Parallelism from the evaluation of operating conditions, gates and stimuli is exploited simultaneously to utilize the high arithmetic computing throughput of GPUs. This way, large-scale design space exploration of AVFS-based systems is enabled. Experimental results demonstrate the efficiency and accuracy of the presented approach showing speedups of three orders of magnitude over conventional time simulation that supports static delays only.

Zusammenfassung

Early life failures in marginal devices are a severe reliability threat in current nano-scaled CMOS devices. While small delay faults are an effective indicator of marginalities, their detection requires special efforts in testing by so-called Faster-than-At-Speed Test (FAST). In a similar way, delay degradation is an indicator that a device reaches the wear-out phase due to aging. Programmable delay monitors provide the possibility to detect gradual performance changes in a system and allow to observe device degradation.This paper presents a unified approach to test small delay faults related to wear-out and early-life failures by reuse of existing programmable delay monitors within FAST. The approach is complemented by a test-scheduling which optimally selects frequencies and delay configurations to significantly increase the fault coverage of small delays and to reduce the test time.

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On-chip instrumentation is mandatory for efficient bring-up, test and diagnosis, post-silicon validation, as well as in-field calibration, maintenance, and fault tolerance. Reconfigurable scan networks (RSNs) provide a scalable and efficient scan-based access mechanism to such instruments. The correct operation of this access mechanism is crucial for all manufacturing, bring-up and debug tasks as well as for in-field operation, but it can be affected by faults and design errors. This work develops for the first time fault-tolerant RSNs such that the resulting scan network still provides access to as many instruments as possible in presence of a fault. The work contributes a model and an algorithm to compute scan paths in faulty RSNs, a metric to quantify its fault tolerance and a synthesis algorithm that is based on graph connectivity and selective hardening of control logic in the scan network. Experimental results demonstrate that fault-tolerant RSNs can be synthesized with only moderate hardware overhead.

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Modern electronic systems become evermore complex, yet remain modular, with integrated circuits (ICs) acting as versatile hardware components at their heart. Electronic design automation (EDA) for ICs has focused traditionally on power, performance, and area. However, given the rise of hardware-centric security threats, we believe that EDA must also adopt related notions like secure by design and secure composition of hardware. Despite various promising studies, we argue that some aspects still require more efforts, for example: effective means for compilation of assumptions and constraints for security schemes, all the way from the system level down to the "bare metal"; modeling, evaluation, and consideration of security-relevant metrics; or automated and holistic synthesis of various countermeasures, without inducing negative cross-effects.In this paper, we first introduce hardware security for the EDA community. Next we review prior (academic) art for EDA-driven security evaluation and implementation of countermeasures. We then discuss strategies and challenges for advancing research and development toward secure composition of circuits and systems.

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Image steganography can have legitimate uses, for example, augmenting an image with a watermark for copyright reasons, but can also be utilized for malicious purposes. We investigate the detection of malicious steganography using neural networkbased classification when images are transmitted through a noisy channel. Noise makes detection harder because the classifier must not only detect perturbations in the image but also decide whether they are due to the malicious steganographic modifications or due to natural noise. Our results show that reliable detection is possible even for state-of-the-art steganographic algorithms that insert stego bits not affecting an images visual quality. The detection accuracy is high (above 85%) if the payload, or the amount of the steganographic content in an image, exceeds a certain threshold. At the same time, noise critically affects the steganographic information being transmitted, both through desynchronization (destruction of information which bits of the image contain steganographic information) and by flipping these bits themselves. This will force the adversary to use a redundant encoding with a substantial number of error-correction bits for reliable transmission, making detection feasible even for small payloads.

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Due to throughput requirements above 1 gigapixel/sec for the real-time compression of modern image and video data streams, parallelism for encoding and decoding is inevitable. To achieve parallel decoding, a well-established technique is to insert markers into the variable length code (VLC) stream. By locating markers, it is then possible to extract the sub-streams that are, in turn, decoded in parallel. The use of markers adversely affects compression especially when a high degree of parallelism is required. In this paper, we propose an architecture of a marker-free parallel decoding approach of VLC streams. Instead of multiple local entropy decoders, the proposed architecture is based on using a single parallel entropy decoder in conjunction with a novel format to construct the VLC stream. The approach runs at high clock rates supporting parallelism to a high number of decoders. A synthesized clock frequency well above 110 MHz is achieved for up to 20 decoders on a medium-sized FPGA.

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With today’s tight timing margins, increasing manufacturing variations, and new defect behaviors in FinFETs, effective yield learning requires detailed information on the population of small delay defects in fabricated chips. Small delay fault diagnosis for yield learning faces two principal challenges: (1) production test responses are usually highly compacted reducing the amount of available failure data, and (2) failure signatures not only depend on the actual defect but also on omnipresent and unknown delay variations. This work presents the very first diagnosis algorithm specifically designed to diagnose timing issues on compacted failure data and under process variations. An innovative combination of variation-invariant structural anal- ysis, GPU-accelerated time-simulation, and variation-tolerant syndrome matching for compacted test responses allows the proposed algorithm to cope with both challenges. Experiments on large benchmark circuits clearly demonstrate the scalability and superior accuracy of the new diagnosis approach.

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Hardware security adds another dimension to the design space, and more and more attention is paid to protect a circuit against various types of attacks like sniffing, spoofing or IP theft. However, all the efforts for security taken by a designer might be sacrificed by afterwards integrating infrastructure for test, diagnosis and reliability management. Especially, access mechanisms like reconfigurable scan networks (RSNs) may open options for side-channel attacks. In this paper, an exact method is presented to verify that a specified RSN does not introduce a new data path which was not already present in the original design. The method uses a matrix-based reachability analysis of the original design and the augmented design.The reachability analysis covers complex functional dependencies, caused by configuring a single scan path as well as multiple sequentially activated scan paths through the RSN. %An accurate computation of the set of functionally possible paths introduced by integrating an RSN into the design is done and used to determine whether any new data paths have been added to the design. This approach adds acceptable runtime to the security verification flow of the design, and shows the designer the introduced possible security violations.

Zusammenfassung

Union-find algorithms form the basis of managing sets of equivalent labels within most connected components labelling algorithms. The new class of single-pass connected components analysis (CCA) algorithms (where a feature vector of each component is extracted during processing) are analysed and compared within this context. Such algorithms have been developed for stream processing, using customised hardware architectures. Many of these use an improved union-find algorithm requiring only a single lookup for its find operation. This paper analyses this optimisation and formally proves that the resulting single lookup connected components algorithm associates each pixel with its correct component when extracting the components’ feature vectors. Analysis of the algorithm led to a new double lookup algorithm that reduces the total number of memory accesses and is a step towards unifying pixel-based methods and run-based methods. State-of-the-art CCA algorithms are compared in terms of the number of memory accesses, which is a limiting factor for hardware-based acceleration, with key implementation trade-offs identified between hardware resources and worst-case processing speed.

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Marginal hardware introduces severe reliability threats throughout the life cycle of a system. Although marginalities may not affect the functionality of a circuit immediately after manufacturing, they can degrade into hard failures and must be screened out during manufacturing test to prevent early life failures. Furthermore, their evolution in the field must be proactively monitored by periodic tests before actual failures occur. In recent years small delay faults have gained increasing attention as possible indicators of marginal hardware. However, small delay faults on short paths may be undetectable even with advanced timing aware ATPG. Faster- than-at-speed test (FAST) can detect such hidden delay faults, but so far FAST has mainly been restricted to manufacturing test. This paper presents a fully autonomous built-in self-test (BIST) approach for FAST, which supports in-field testing by appropriate strategies for test generation and response compac- tion. In particular, the required test frequencies for hidden delay fault detection are selected, such that hardware overhead and test time are minimized. Furthermore, test response compaction handles the large number of unknowns (X-values) on long paths by storing intermediate MISR-signatures in a small on-chip memory for later analysis using X-canceling transformations. A comprehensive experimental study demonstrates the effectiveness of the presented approach. In particular, the impact of the considered fault size is studied in detail.

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The evaluation of fault attacks on security-critical hardware implementations of cryptographic primitives is an important concern. In such regards, we have created a framework for automated construction of fault attacks on hardware realization of ciphers. The framework can be used to quickly evaluate any cipher implementations, including any optimisations. It takes the circuit description of the cipher and the fault model as input. The output of the framework is a set of algebraic equations, such as conjunctive normal form (CNF) clauses, which is then fed to a SAT solver. We consider both attacking an actual implementation of a cipher on an field-programmable gate array (FPGA) platform using a fault injector and the evaluation of an early design of the cipher using idealized fault models. We report the successful application of our hardware-oriented framework to a collection of ciphers, including the advanced encryption standard (AES), and the lightweight block ciphers LED and PRESENT. The corresponding results and a discussion of the impact to different fault models on our framework are shown. Moreover, we report significant improvements compared to similar frameworks, such as speedups or more advanced features. Our framework is the first algebraic fault attack (AFA) tool to evaluate the state-of-the art cipher LED-64, PRESENT and full-scale AES using only hardware-oriented structural cipher descriptions.

Zusammenfassung

Embedded systems design has lately become particularly challenging due to fast increasing system complexities, realtime demands and reliability requirements. At the same time, designs are constrained by stringent power budgets, limited memory capacity and short time-to-markets. Recently, various methods to automatically generate, analyze, and optimize different stages of the design flow have been investigated. The evaluation of these methods, however, often focuses on a single associated field of research and thus may not consider the domain-crossing effects that stem from the interaction of different design methods. This paper presents a complete and fully automated workflow that covers model-based generation, analysis, and optimization aspects for embedded firmware and demonstrates it on a virtual system prototype of a typical inertial sensor node. We illustrate the integration of different design and evaluation methods on a realistic example and show potential opportunities for the application of gathered information in order to improve the design.

Zusammenfassung

Reconfigurable Scan Networks (RSNs) allow flexible access to embedded instruments for post-silicon test, validation and debug or diagnosis. The increased observability and controllability of registers inside the circuit can be exploited by an attacker to leak or corrupt critical information. Precluding such security threats is of high importance but difficult due to complex data flow dependencies inside the reconfigurable scan network as well as across the underlying circuit logic. This work proposes a method that fine-granularly computes dependencies over circuit logic and the RSN. These dependencies are utilized to detect security violations for a given insecure RSN, which is then transformed into a secure RSN. Experimental results demonstrate the applicability of the method to large academical and industrial designs. Additionally, we report on the required effort to mitigate found security violations which also motivates the necessity to consider the circuit logic in addition to pure scan paths.

Zusammenfassung

In CMOS technology first-order parametric faults during manufacturing can exhibit severe changes in the timing as well as in the functional behavior of cells. Since these faults are hard to detect by conventional tests, the accurate simulation of these low-level faults plays an important role for test validation. However, pure low-level fault simulation approaches impose a high computational complexity that can quickly become inapplicable to larger simulation problems due to limitations in scalability. In this paper, the first parallel multi-level fault simulation approach on graphics processing units (GPUs) is presented. The approach utilizes both logic level and switch level descriptions concurrently in a mixed-abstraction timing simulation. The abstraction is lowered in user-defined so-called regions of interest that locally increase the modeling accuracy enabling low-level first-order parametric fault injection. Resulting signal waveforms are transformed between the different abstractions transparently. This way a fast, versatile and efficient multi-level fault simulation approach on GPUs is created that scales for designs with millions of cells while achieving high simulation throughput with runtime savings of up to 84% compared to full switch level simulations.

Zusammenfassung

Current nanometer CMOS circuits show an increasing sensitivity to deviations in first-order parameters and suffer from process variations during manufacturing. To properly assess and support test validation of digital designs, low-level fault simulation approaches are utilized to accurately capture the behavior of CMOS cells under parametric faults and process variations as early as possible throughout the design phase. However, low-level simulation approaches exhibit a high computational complexity, especially when variation has to be taken into account. In this work a high-throughput parallel fault simulation at switch level is presented. First-order electrical parameters are utilized to capture CMOS-specific functional and timing behavior of complex cells allowing to model faults with transistor granularity and without the need of logic abstraction. Furthermore, variation modeling in cells and transistor devices enables broad and efficient variation analyses of faults over many circuit instances for the first time. The simulation approach utilizes massive parallelization on Graphics Processing Units (GPUs) by exploiting parallelism from cells, stimuli, faults and circuit instances. Despite the lower abstraction levels of the approach, it processes designs with millions of gates and outperforms conventional fault simulation at logic level in terms of speed and accuracy.

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The chapter discusses the background for the most demanding dependability hallenges for reconfigurable processors in many-core systems and presents a dependable runtime reconfigurable processor for high reliability. It uses an adaptive modular redundancy technique that guarantees an application-specified level of reliability under changing SEU rates by budgeting the effective critical bits among all kernels and all accelerators of an application. This allows to deploy reconfigurable processors in harsh environments without statically protecting them.

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This paper presents a hardware efficient pixel-domain just-noticeable difference (JND) model and its hardware architecture implemented on an FPGA. This JND model architecture is further proposed to be part of a low complexity pixel-domain perceptual image coding architecture, which is based on downsampling and predictive coding. The downsampling is performed adaptively on the input image based on regions-of-interest (ROIs) identified by measuring the downsampling distortions against the visibility thresholds given by the JND model. The coding error at any pixel location can be guaranteed to be within the corresponding JND threshold in order to obtain excellent visual quality. Experimental results show the improved accuracy of the proposed JND model in estimating visual redundancies compared with classic JND models published earlier. Compression experiments demonstrate improved rate-distortion performance and visual quality over JPEG-LS as well as reduced compressed bit rates compared with other standard codecs such as JPEG 2000 at the same peak signal-to-perceptible-noise ratio (PSPNR). FPGA synthesis results targeting a mid-range device show very moderate hardware resource requirements and over 100 Megapixel/s throughput of both the JND model and the perceptual encoder.

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Fault attacks are an essential threat to security of cryptographic functions. This chapter focuses on underlying concepts of fault attacks on cryptographic circuit implementations. Rather than targeting a specific cipher, the presented approach aims at an automatic construction of fault attacks from the circuit description. A Boolean formula that describes the circuit operation and the fault injection is generated and handed over to a SAT solver; an adversary can deduce protected information, e.g., the secret key, from the solution. The chapter also includes a comparison of the described methodology with other existing fault attack frameworks that are not specifically targeted towards circuit implementations.

Zusammenfassung

Aging monitors can indicate the wear-out phase of a semi-conductor device before it will actually fail, and allow the use of integrated circuits in applications with high safety and reliability demands. In the early phase of the lifecycle of integrated systems, small delay faults may indicate reliability problems and early life failures, even if they are smaller than the slack of any path and neither alter the functional behavior of a system nor violate any aging guardband. One option to detect this type of hidden delay faults (HDFs) is the application of a faster-than-at-speed-test (FAST). This paper shows that aging monitors can be extended at low cost to achieve high HDF test coverage with a reduction in test time during FAST. The result is a unified strategy to improve the reliability in both early and late phases of the system lifecycle.

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High scan shift power often leads to excessive heat as well as other severe problems such as shift timing failures. Partial shift (shifting only a subset of scan chains at a time) is a widely-adopted general approach for mitigating shift power problems. Although partial shift is effective in avoiding excessive heat by reducing global switching activity, we show for the first time that it may actually worsen shift clock skews, thus increasing the risk of shift timing failures. This paper addresses this problem with an innovative method, namely Clock-Skew-Aware Scan Chain Grouping (CSA-SCG). CSA-SCG properly groups scan chains to be shifted simultaneously so as to reduce the imbalance of switching activity around the clock paths for neighboring scan flip-flops in the scan chains. Experiments on large ITC'99 benchmark circuits demonstrate the effectiveness of CSA-SCG for reducing scan shift clock skews to lower the risk of shift timing failures in partial shift.

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Self-awareness allows autonomous systems the dynamic adaptation to changing states of the hardware platform and the optimal usage of available computing resources. This demands concurrent, periodical, or on-demand monitoring and testing of the hardware structures to detect and classify deviations from the nominal behavior and appropriate reactions. This survey discusses suitable self-test, self-checking, and self-diagnosis methods for the realization of self-awareness and presents two case studies in which such methods are applied at different levels.

Zusammenfassung

Device aging is an important concern in nanoscale designs. Due to aging the electrical behavior of transistors embedded in an integrated circuit deviates from original intended one. This leads to performance degradation in the underlying device, and the ultimate device failure. This effect is exacerbated in emerging technologies. To be able to tailor effective aging mitigation schemes and improve the reliability of devices realized in cutting edge technologies, there is a need to accurately study the effect of aging in high performance industrial applications. According, this paper targets a high performance SRAM memory realized in 14nm FinFET technology and depicts how aging degrades the individual components of this memory as well as the interaction between them. Aging mitigation is critical not only from device reliability point of view but also regarding device security perspectives. It is essential to assure the security of the sensitive tasks performed by the security-sensitive circuits and to guarantee the security of information stored within these devices in the presence of aging. Accordingly in this paper, we also focus on aging-related security concerns and present the cases in which aging need to considered to preserve security.

Zusammenfassung

Modern systems-on-chip (SoC) designs are requiring more and more infrastructure for validation, debug, volume test as well as in-field maintenance and repair. Reconfigurable scan networks (RSNs), as allowed by IEEE 1687 (IJTAG) standard, provide flexible access to the infrastructure with low access latency. However, they can also pose a security threat to the system, by leaking information about the system state. In this paper, we present a protection method that monitors access and checks for violations of security properties online. The method prevents unauthorized access to sensitive and secure instruments. In addition, the system integrator can specify more complex security requirements, including giving multiple users different access privileges. Simultaneous accesses to multiple instruments, that would expose sensitive data to an untrusted core (e.g. from 3rd party vendors) or instrument, can be prohibited. The method does not require any change to the RSN architecture and is easily integrable with IP core designs. The area overhead with respect to the size of the RSN is below 6% and scales well with larger networks.

Zusammenfassung

Reconfigurable Scan Networks (RSNs) allow flexible access to embedded instruments for post-silicon validation and debug or diagnosis. However, this scan infrastructure can also be exploited to leak or corrupt critical information as observation and controllability of registers deep inside the circuit are increased. Securing an RSN is mandatory for maintaining safe and secure circuit operations but difficult due to its complex data flow dependencies. This work proposes a method that detects security violations and transforms a given insecure RSN into a secure RSN for which the secure data flow as specified by a user is guaranteed by construction. The presented method is guided by user-defined cost functions that target e.g. test performance or wiring cost. We provide a case study and experimental results demonstrating the applicability of the method to large designs with low runtime.

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Automotive microelectronics are gaining great impact with the advent of electromobility and self-driving cars. Being a mass market, cost sensitivity is a major concern, while the functional safety, security, stringent run-time requirements and reliable operation are still major challenges. Automotive Reliability and Test (ART) has been the most popular topic at the recent IEEE International Test Conferences, and the ART Workshop 2016 in conjunction with ITC was extremely popular. This special issue of IEEE Design&Test originates from this workshop and deals with the most significant topics in automotive reliability and test.

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Approximate computing exploits the inherent error resilience of many applications to optimize power consumption, run time, or chip area. Especially in audio, image, and video processing, but also in data mining or resource allocation tasks, approximate results are "good enough" for many application domains and may be hard to distinguish from perfect results. But even in domains where accurate results are required, approximate computing can be applied in various layers of the computing stack. Approximate computing promises to significantly increase computing efficiency (especially performance per power) and power density and is therefore of particular interest for embedded computing where these metrics are key.

Zusammenfassung

Timing-accurate simulation of circuits is an important task in design validation of modern nano-scale CMOS circuits. With shrinking technology nodes, detailed simulation models down to transistor level have to be considered. While conventional simulation at logic level lacks the ability to accurately model timing behavior for complex cells, more accurate simulation at lower abstraction levels becomes computationally expensive for larger designs. This work presents the first parallel multi-level waveform-accurate timing simulation approach on graphics processing units (GPUs). The simulation uses logic and switch level abstraction concurrently, thus allowing to combine their advantages by trading off speed and accuracy. The abstraction can be lowered in arbitrary regions of interest to locally increase the accuracy. Waveform transformations allow for transparent switching between the abstraction levels. With the utilization of GPUs and thoughtful unification of algorithms and data structures, a fast and versatile high-throughput multi-level simulation is obtained that is scalable for millions of cells while achieving runtime savings of up to 89% compared to full simulation at switch level.

Zusammenfassung

Design, production and operation of modern system-on-chips rely on integrated instruments, which range from simple sensors to complex debug interfaces and design-for-test (DfT) structures. Reconfigurable scan networks (RSNs) as defined in IEEE Std. 1687-2014 provide an efficient access mechanism to such instruments. It is essential to test the access mechanism itself before it can be used for test, diagnosis, validation, calibration or runtime monitoring. Realistic fault mechanisms in RSNs are hard to test due to their high sequential depth and limited controllability and observability via serial scan ports. We present a novel low-cost DfT modification specifically designed for RSNs that enhances the observability of shadow registers. Furthermore, we present different test methods for stuck-at and more realistic gate-level fault models like flip-flop- internal and bridge faults. Experimental results demonstrate the effectiveness of the presented DfT modification and test methods.

Zusammenfassung

The accessibility of on-chip embedded infrastructure for test, reconfiguration, or debug poses a serious security problem. Access mechanisms based on IEEE Std 1149.1 (JTAG), and especially reconfigurable scan networks (RSNs), as allowed by IEEE Std 1500, IEEE Std 1149.1-2013, and IEEE Std 1687 (IJTAG), require special care in the design and development. This work studies the threats to trustworthy data transmission in RSNs posed by untrusted components within the RSN and external interfaces. We propose a novel scan pattern generation method that finds trustworthy access sequences to prevent sniffing and spoofing of transmitted data in the RSN. For insecure RSNs, for which such accesses do not exist, we present an automated transformation that improves the security and trustworthiness while preserving the accessibility to attached instruments. The area overhead is reduced based on results from trustworthy access pattern generation. As a result, sensitive data is not exposed to untrusted components in the RSN, and compromised data cannot be injected during trustworthy accesses.

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Iterative solvers like the Preconditioned Conjugate Gradient (PCG) method are widely-used in compute-intensive domains including science and engineering that often impose tight accuracy demands on computational results. At the same time, the error resilience of such solvers may change in the course of the iterations, which requires careful adaption of the induced approximation errors to reduce the energy demand while avoiding unacceptable results. A novel adaptive method is presented that enables iterative Preconditioned Conjugate Gradient (PCG) solvers on Approximate Computing hardware with high energy efficiency while still providing correct results. The method controls the underlying precision at runtime using a highly efficient fault tolerance technique that monitors the induced error and the quality of intermediate computational results.

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Runtime reconfigurable architectures based on Field-Programmable Gate Arrays (FPGAs) allow area- and power-efficient acceleration of complex applications. However, being manufactured in latest semiconductor process technologies, FPGAs are increasingly prone to aging effects, which reduce the reliability and lifetime of such systems. Aging mitigation and fault tolerance techniques for the reconfigurable fabric become essential to realize dependable reconfigurable architectures. This article presents an accelerator diversification method that creates multiple configurations for runtime reconfigurable accelerators that are diversified in their usage of Configurable Logic Blocks (CLBs). In particular, it creates a minimal number of configurations such that all single-CLB and some multi-CLB faults can be tolerated. For each fault we ensure that there is at least one configuration that does not use that CLB. Secondly, a novel runtime accelerator placement algorithm is presented that exploits the diversity in resource usage of these configurations to balance the stress imposed by executions of the accelerators on the reconfigurable fabric. By tracking the stress due to accelerator usage at runtime, the stress is balanced both within a reconfigurable region as well as over all reconfigurable regions of the system. The accelerator placement algorithm also considers faulty CLBs in the regions and selects the appropriate configuration such that the system maintains a high performance in presence of multiple permanent faults. Experimental results demonstrate that our methods deliver up to 3.7x higher performance in presence of faults at marginal runtime costs and 1.6x higher MTTF than state-of-the-art aging mitigation methods.

Zusammenfassung

With the ever increasing process variability in recent technology nodes, path delay fault testing of digital integrated circuits has become a major challenge. A randomly chosen long path often has no robust test and many of the existing non-robust tests are likely invalidated by process variations. To generate path delay fault tests that are more tolerant towards process variations, the delay test generation must evaluate different non-robust tests and only those tests that sensitize the target path with a sufficiently high probability in presence of process variations must be selected. This requires a huge number of probability computations for a large number of target paths and makes the development of very efficient approximation algorithms mandatory for any practical application. In this paper, a novel and efficient probabilistic sensitization analysis is presented which is used to extract a small subcircuit for a given test vector-pair. The probability that a target path is sensitized by the vector-pair is computed efficiently and without significant error by a Monte-Carlo simulation of the subcircuit.

Zusammenfassung

A large amount of on-chip infrastructure, such as design-for-test, debug, monitoring, or calibration, is required for the efficient manufacturing, debug, and operation of complex hardware systems. The access to such infrastructure poses severe system safety and security threats since it may constitute a side-channel exposing internal state, sensitive data, or IP to attackers. Reconfigurable scan networks (RSNs) have been proposed as a scalable and flexible scan-based access mechanism to on-chip infrastructure. The increasing number and variety of integrated infrastructure as well as diverse access constraints over the system lifetime demand for systematic methods for the specification and formal verification of access protection and security properties in RSNs. This work presents a novel method to specify and verify fine-grained access permissions and restrictions to instruments attached to an RSN. The permissions and restrictions are transformed into predicates that are added to a formal model of a given RSN to prove which access properties hold or do not hold.

Zusammenfassung

In order to tolerate faults that emerge in operating Networks-on-Chip, diagnosis techniques are employed for fault detection and localization. On various network layers, diverse diagnosis methods can be employed which differ in terms of their impact on network performance (e.g. by operating concurrently vs. pre-empting regular network operation) and the quality of diagnostic results. In this contribution, we show how diagnosis techniques of different network layers of a Network-on-Chip can be combined into multi-layer solutions. We present the cross-layer information flow used for the interaction between the layers and show the resulting benefit of the combination compared to layer-specific diagnosis. For evaluation, we investigate the diagnosis quality and the impact on system performance to explore the entire design space of layer-specific techniques and their multi-layer combinations. We identify pareto-optimal combinations that offer an increase of system performance by a factor of four compared to the single-layer diagnosis.

Zusammenfassung

Delay fault simulation is an essential task during test pattern generation and reliability assessment of electronic circuits. With the high sensitivity of current nano-scale designs towards even smallest delay deviations, the simulation of small gate delay faults has become extremely important. Since these faults have a subtle impact on the timing behavior, traditional fault simulation approaches based on abstract timing models are not sufficient. Furthermore, the detection of these faults is compromised by the ubiquitous variations in the manufacturing processes, which causes the actual fault coverage to vary from circuit instance to circuit instance, and makes the use of timing accurate methods mandatory. However, the application of timing accurate techniques quickly becomes infeasible for larger designs due to excessive computational requirements. In this work, we present a method for fast and waveformaccurate simulation of small delay faults on graphics processing units with exceptional computational performance. By exploiting multiple dimensions of parallelism from gates, faults, waveforms and circuit instances, the proposed approach allows for timing-accurate and exhaustive small delay fault simulation under process variation for designs with millions of gates.

Zusammenfassung

In recent years early life failures have caused several product recalls in semiconductor and automotive industries associated with a loss of billions of dollars. They can be traced back to various root-causes. In embedded or cyber-physical systems, the interaction with the environment and the behavior of the hardware/software interface are hard to predict, which may lead to unforeseen failures. In addition to that, defects that have escaped manufacturing test or “weak” devices that cannot stand operational stress may for example cause unexpected hardware problems in the early life of a system. The special session focuses on the first aspect. The first contribution discusses how the interaction with the environment in cyberphysical systems can be appropriately modeled and tested. The second presentation then deals with a cross-layer approach identifying problems at the hardware/software interface which cannot be compensated by the application and must therefore be targeted by specific tests.

Zusammenfassung

Small delay faults receive more and more attention, since they may indicate a circuit reliability marginality even if they do not violate the timing at the time of production. At-speed test and faster-than-at-speed test (FAST) are rather expensive tasks to test for such faults. The paper at hand avoids complex on-chip structures or expensive high-speed ATE for test response evaluation, if aging monitors which are integrated into the device under test anyway are reused. The main challenge in reusing aging monitors for FAST consists in possible false alerts at higher frequencies. While a certain test vector pair makes a delay fault observable at one monitor, it may also exceed the time slack in the fault free case at a different monitor which has to be masked. Therefore, a multidimensional optimizing problem has to be solved for minimizing the masking overhead and the number of test vectors while maximizing delay fault coverage.

Zusammenfassung

Excessive IR-drop during scan shift can cause localized IR-drop around clock buffers and introduce dynamic clock skew. Excessive clock skew at neighboring scan flip-flops results in hold or setup timing violations corrupting test stimuli or test responses during shifting. We introduce a new method to assess the risk of such test data corruption at each scan cycle and flip-flop. The most likely cases of test data corruption are mitigated in a non-intrusive way by selective test data manipulation and masking of affected responses. Evaluation results show the computational feasibility of our method for large benchmark circuits, and demonstrate that a few targeted pattern changes provide large potential gains in shift safety and test time with negligible cost in fault coverage.

Zusammenfassung

IEEE Std. 1687, or IJTAG, defines flexible serial scan-based architectures for accessing embedded instruments efficiently. In this paper, we present a novel test architecture that employs IEEE Std. 1687 together with an efficient test controller to carry out 3D-IC testing autonomously. The test controller can deliver parallel test data for the IEEE Std. 1687 structures and the cores under test, and provide required control signals to control the whole test procedure. This design can achieve at-speed, autonomous and programmable testing in 3D-ICs. Experimental results show that the additional area and test cycle overhead of this architecture is small considering its autonomous test capability.

Zusammenfassung

IR-drop induced false capture failures and test clock stretch are severe problems in at-speed scan testing. We propose a new method to efficiently and accurately identify these problems. For the first time, our approach considers the additional dynamic power caused by glitches, the spatial and temporal distribution of all toggles, and their impact on both logic paths and the clock tree without time-consuming electrical simulations.

Zusammenfassung

Reconfigurable Networks-on-Chip (NoCs) allow discarding the corrupted ports of a defective switch instead of deactivating it entirely, and thus enable fine-grained reconfiguration of the network, making the NoC structures more robust. A prerequisite for such a fine-grained reconfiguration is to identify the corrupted port of a faulty switch. This paper presents a functional diagnosis approach which extracts structural fault information from functional tests and utilizes this information to identify the broken functions/ports of a defective switch. The broken parts are discarded while the remaining functions are used for the normal operation. The non-intrusive method introduced is independent of the switch architecture and the NoC topology and can be applied for any type of structural fault. The i diagnostic resolution of the functional test is so high that for nearly 64% of the faults in the example switch only a single port has to be switched off. As the remaining parts stay completely functional, the impact of faults on throughput and performance is minimized.

Zusammenfassung

On-chip infrastructure is an essential part of today’s complex designs and enables their cost-efficient manufacturing and operation. The diversity and high number of infrastructure elements demands flexible and low-latency access mechanisms, such as reconfigurable scan networks (RSNs). The correct operation of the infrastructure access itself is highly important for the test of the system logic, its diagnosis, debug and bring-up, as well as post-silicon validation. Ensuring correct operation requires the thorough testing of the RSN. Because of sequential and combinational dependencies in RSN accesses, test generation for general RSNs is computationally very difficult and requires dedicated test strategies. This paper explores different test strategies for general RSNs and discusses the achieved structural fault coverage. Experimental results show that the combination of functional test heuristics together with a dedicated RSN test pattern generation approach significantly outperforms the test quality of a standard ATPG tool.

Zusammenfassung

In order to reduce the number of defective parts and increase yield, especially in early stages of production, systematic defects must be identified and corrected as soon as possible. This paper presents a technique to move defect classification to the earliest phase of volume testing without any special diagnostic test patterns. A neural-network-based fault classifier is described, which is able to raise a warning, if the frequency of certain defect mechanisms increases. Only in this case more sophisticated diagnostic patterns or the even more expensive physical failure analysis have to be applied. The fault classification method presented here is able to extract underlying fault types with high confidence by identifying relevant features from the circuit topology and from logic simulation.

Zusammenfassung

Deviations in the first-order parameters of CMOS cells can lead to severe errors in the functional and time domain. With increasing sensitivity of these parameters to manufacturing defects and variation, parametric and parasitic-aware fault simulation is becoming crucial in order to support test pattern generation. Traditional approaches based on gate-level models are not sufficient to represent and capture the impact of deviations in these parameters in either an efficient or accurate manner. Evaluation at electrical level, on the other hand, severely lacks execution speed and quickly becomes inapplicable to larger designs due to high computational demands. This work presents a novel fault simulation approach considering first-order parameters in CMOS circuits to explicitly capture CMOS-specific behavior in the functional and time domain with transistor granularity. The approach utilizes massive parallelization in order to achieve high-throughput acceleration on Graphics Processing Units (GPUs) by exploiting parallelism of cells, stimuli and faults. Despite the more precise level of abstraction, the simulator is able to process designs with millions of gates and even outperforms conventional simulation at logic level in terms of modeling accuracy and simulation speed.

Zusammenfassung

A new technique is presented that allows to execute the preconditioned conjugate gradient (PCG) solver on approximate hardware while ensuring correct solver results. This technique expands the scope of approximate computing to scientific and engineering applications. The changing error resilience of PCG during the solving process is exploited by different levels of approximation which trade off numerical accuracy and hardware utilization. Such approximation levels are determined at runtime by periodically estimating the error resilience. An efficient fault tolerance technique allows reductions in hardware utilization by ensuring the continued exploitation of maximum allowed energy-accuracy trade-offs. Experimental results show that the hardware utilization is reduced on average by 14.5% and by up to 41.0% compared to executing PCG on accurate hardware.

Zusammenfassung

Approximate computing in hardware and software promises significantly improved computational performance combined with very low power and energy consumption. This goal is achieved by both relaxing strict requirements on accuracy and precision, and by allowing a deviating behavior from exact Boolean specifications to a certain extent. Today, approximate computing is often limited to applications with a certain degree of inherent error tolerance, where perfect computational results are not always required. However, in order to fully utilize its benefits, the scope of applications has to be significantly extended to other compute-intensive domains including science and engineering. To meet the often rather strict quality and reliability requirements for computational results in these domains, the use of appropriate characterization and fault tolerance measures is highly required. In this paper, we evaluate some of the available techniques and how they may extend the scope of application for approximate computing.

Zusammenfassung

Das Projekt ”SHIVA: Sichere Hardware in der Informationsverarbeitung“ ist Teil des Forschungsprogramms ”IKTSicherheit für weltweit vernetzte vertrauenswürdige Infrastrukturen“ der Baden-Württemberg Stiftung. Ziel des Projekts sind die Erforschung von Entwurfs- und Verifikationsmethoden zur Steigerung der Sicherheit mikroelektronischer Hardware, beispielsweise aus der Automobilelektronik, der Medizintechnik oder auch der Fertigungstechnik. Es soll damit die missbräuchliche Verwendung nicht-funktionaler Hardware-Infrastruktur zur Beobachtung interner sensibler Daten, verwendeter Verfahren und Prozesse sowie zu Angriffen auf das geistige Eigentum an der Hardware ausgeschlossen werden. Das Projekt ist eine Kooperation des Instituts für Technische Informatik (ITI) der Universität Stuttgart und des Lehrstuhls für Rechnerarchitektur der Universität Freiburg. Dieser Beitrag stellt die Projektziele und erste Forschungsergebnisse vor.

Zusammenfassung

Approximate computing algorithms cover a wide range of different applications and the boundaries to domains like variable-precision computing, where the precision of the computations can be online adapted to the needs of the application 1, 2, as well as probabilistic and stochastic computing 3, which incorporate stochastic processes and probability distributions in the target computations, are sometimes blurred. The central idea of purely algorithm-based approximate computing is to transform algorithms, without necessarily requiring approximate hardware, to trade-off accuracy against energy. Early termination of algorithms that exhibit incremental refinement 4 reduces iterations at the cost of accuracy. Loop perforation 5 approximates iteratively-computed results by identifying and reducing loops that contribute only insignificantly to the solution. Another group of approximate algorithms is represented by neural networks, which can be trained to mimic certain algorithms and to compute approximate results 6. Today, approximate computing is predominantly proposed for applications in multimedia and signal processing with a certain degree of inherent error tolerance. However, in order to fully utilize the benefits of these architectures, the scope of applications has to be significantly extended to other computeintensive tasks, for instance, in science and engineering. Such an extension requires that the allowed error or the required minimum precision of the application is either known beforehand or reliably determined online to deliver trustworthy and useful results. Errors outside the allowed range have to be reliably detected and tackled by appropriate fault tolerance measures.

Zusammenfassung

We propose a fault tolerance approach for sparse matrix operations that detects and implicitly locates errors in the results for efficient local correction. This approach reduces the runtime overhead for fault tolerance and provides high error coverage. Existing algorithm-based fault tolerance approaches for sparse matrix operations detect and correct errors, but they often rely on expensive error localization steps. General checkpointing schemes can induce large recovery cost for high error rates. For sparse matrix-vector multiplications, experimental results show an average reduction in runtime overhead of 43.8%, while the error coverage is on average improved by 52.2% compared to related work. The practical applicability is demonstrated in a case study using the iterative Preconditioned Conjugate Gradient solver. When scaling the error rate by four orders of magnitude, the average runtime overhead increases only by 31.3% compared to low error rates.

Zusammenfassung

Unknown (X) values emerge during the design process as well as during system operation and test application. X-sources are for instance black boxes in design models, clockdomain boundaries, analog-to-digital converters, or uncontrolled or uninitialized sequential elements. To compute a test pattern for a given fault, well-defined logic values are required both for fault activation and propagation to observing outputs. In presence of X-values, conventional test generation algorithms, based on structural algorithms, Boolean satisfiability (SAT), or BDD-based reasoning may fail to generate test patterns or to prove faults untestable. This work proposes the first efficient stuck-at and transitiondelay fault test generation algorithm able to prove testability or untestability of faults in presence of X-values. It overcomes the principal pessimism of conventional algorithms when X-values are considered by mapping the test generation problem to the satisfiability of Quantified Boolean Formulae (QBF). Experiments on ISCAS benchmarks and larger industrial circuits investigate the increase in fault coverage for conventional deterministic and potential detection requirements for both randomized and clustered X-sources.

Zusammenfassung

Failure diagnosis of field returns typically requires high quality test stimuli and assumes that tests can be repeated. For intermittent faults with fault activation conditions depending on the physical environment, the repetition of tests cannot ensure that the behavior in the field is also observed during diagnosis, causing field returns diagnosed as no-trouble-found. In safety critical applications, self-checking circuits, which provide concurrent error detection, are frequently used. To diagnose intermittent and transient faulty behavior in such circuits, we use the stored encoded circuit outputs in case of a failure (called signatures) for later analysis in diagnosis. For the first time, a diagnosis algorithm is presented that is capable of performing the classification of intermittent or transient faults using only the very limited amount of functional stimuli and signatures observed during operation and stored on chip. The experimental results demonstrate that even with these harsh limitations it is possible to distinguish intermittent from transient faulty behavior. This is essential to determine whether a circuit in which failures have been observed should be subject to later physical failure analysis, since intermittent faulty behavior has been diagnosed. In case of transient faulty behavior, it may still be operated reliably.

Zusammenfassung

Small gate delay faults (SDFs) are not detectable at-speed, if they can only be propagated along short paths. These hidden delay faults (HDFs) do not influence the circuit’s behavior initially, but they may indicate design marginalities leading to early-life failures, and therefore they cannot be neglected. HDFs can be detected by faster-than-at-speed test (FAST), where typically several different frequencies are used to maximize the coverage. A given set of test patterns P potentially detects a HDF if it contains a test pattern sensitizing a path through the fault site, and the efficiency of FAST can be measured as the ratio of actually detected HDFs to potentially detected HDFs. The paper at hand targets maximum test efficiency with a minimum number of frequencies. The procedure starts with a test set for transition delay faults and a set of preselected equidistant frequencies. Timing-accurate simulation of this initial setup identifies the hard-to-detect faults, which are then targeted by a more complex timing-aware ATPG procedure. For the yet undetected HDFs, a minimum number of frequencies are determined using an efficient hypergraph algorithm. Experimental results show that with this approach, the number of test frequencies required for maximum test efficiency can be reduced considerably. Furthermore, test set inflation is limited as timing-aware ATPG is only used for a small subset of HDFs.

Zusammenfassung

IR-drop induced by launch switching activity (LSA) in capture mode during at-speed scan testing increases delay along not only logic paths (LPs) but also clock paths (CPs). Excessive extra delay along LPs compromises test yields due to false capture failures, while excessive extra delay along CPs compromises test quality due to test clock stretch. This paper is the first to mitigate the impact of LSA on both LPs and CPs with a novel LCPA (Logic/Clock-Path-Aware) at-speed scan test generation scheme, featuring (1) a new metric for assessing the risk of false capture failures based on the amount of LSA around both LPs and CPs, (2) a procedure for avoiding false capture failures by reducing LSA around LPs or masking uncertain test responses, and (3) a procedure for reducing test clock stretch by reducing LSA around CPs. Experimental results demonstrate the effectiveness of the LCPA scheme in improving test yields and test quality.

Zusammenfassung

Aging effects in nano-scale CMOS circuits impair the reliability and Mean Time to Failure (MTTF) of embedded systems. Especially for FPGAs that are manufactured in the latest technology node, aging is a major concern. We introduce the first cross-layer aging-aware placement method for accelerators in FPGA-based runtime reconfigurable architectures. It optimizes stress distribution by accelerator placement at runtime, i.e. to which reconfigurable region an accelerator shall be reconfigured. Additionally, it optimizes logic placement at synthesis time to diversify the resource usage of individual accelerators, i.e. which CLBs of a reconfigurable region shall be used by an accelerator. Both layers together balance the intra- and inter-region stress induced by the application workload at negligible performance cost. Experimental results show significant reduction of maximum stress of up to 64% and 35%, which leads to up to 177% and 14% MTTF improvement relative to state-of- the-art methods w.r.t. HCI and BTI aging, respectively.

Zusammenfassung

Linear system solvers are an integral part for many different compute-intensive applications and they benefit from the compute power of heterogeneous computer architectures. However, the growing spectrum of reliability threats for such nano-scaled CMOS devices makes the integration of fault tolerance mandatory. The preconditioned conjugate gradient (PCG) method is one widely used solver as it finds solutions typically faster compared to direct methods. Although this iterative approach is able to tolerate certain errors, latest research shows that the PCG solver is still vulnerable to transient effects. Even single errors, for instance, caused by marginal hardware, harsh environments, or particle radiation, can considerably affect execution times, or lead to silent data corruption. In this work, a novel fault-tolerant PCG solver with extremely low runtime overhead is proposed. Since the error detection method does not involve expensive operations, it scales very well with increasing problem sizes. In case of errors, the method selects between three different correction methods according to the identified error. Experimental results show a runtime overhead for error detection ranging only from 0.04% to 1.70%.

Zusammenfassung

Networks-on-chip are inherently fault tolerant or at least gracefully degradable as both, connectivity and amount of resources, provide some useful redundancy. These properties can only be exploited extensively if test and diagnosis techniques support fault detection and error containment in an optimized way. On the one hand, all faulty components have to be isolated, and on the other hand, remaining fault-free functionalities have to be kept operational. In this contribution, behavioral end-to-end error detection is considered together with functional test methods for switches and gate level diagnosis to locate and to isolate faults in the network in an efficient way with low time overhead.

Zusammenfassung

Circuit aging causes a performance degradation and eventually a functional failure. It depends on the workload and the environmental condition of the system, which are hard to predict in early design phases resulting in pessimistic worst case design. Existing delay monitoring schemes measure the remaining slack of paths in the circuit, but cause a significant hardware penalty including global wiring. More importantly, the low sensitization ratio of long paths in applications may lead to a very low measurement frequency or even an unmonitored timing violation. In this work, we propose a delay monitor placement method by analyzing the topological circuit structure and sensitization of paths. The delay monitors are inserted at meticulously selected positions in the circuit, named observation points (OPs). This OP monitor placement method can reduce the number of inserted monitors by up to 98% compared to a placement at the end of long paths. The experimental validation shows the effectiveness of this aging indication, i.e. a monitor issues a timing alert always earlier than any imminent timing failure.

Zusammenfassung

Linear system solvers are key components of many scientific applications and they can benefit significantly from modern heterogeneous computer architectures. However, such nano-scaled CMOS devices face an increasing number of reliability threats, which make the integration of fault tolerance mandatory. The preconditioned conjugate gradient method (PCG) is a very popular solver since it typically finds solutions faster than direct methods, and it is less vulnerable to transient effects. However, as latest research shows, the vulnerability is still considerable. Even single errors caused, for instance, by marginal hardware, harsh operating conditions or particle radiation can increase execution times considerably or corrupt solutions without indication. In this work, a novel and highly efficient fault-tolerant PCG method is presented. The method applies only two inner products to reliably detect errors. In case of errors, the method automatically selects between roll-back and efficient on-line correction. This significantly reduces the error detection overhead and expensive re-computations.

Zusammenfassung

Achieving system-level dependability is a demanding task. The manifold requirements and dependability threats can no longer be statically addressed at individual abstraction layers. Instead, all components of future multi-processor systems-on-chip (MPSoCs) have to contribute to this common goal in an adaptive manner. In this paper we target a generic heterogeneous MPSoC that combines general purpose processors along with dedicated application-specific hard-wired accelerators, fine-grained reconfigurable processors, and coarse-grained reconfigurable architectures. We present different reactive and proactive measures at the layers of the runtime system (online resource management), system architecture (global communication), micro architecture (individual tiles), and gate netlist (tile-internal circuits) to address dependability threats.

Zusammenfassung

Modern VLSI designs incorporate a high amount of instrumentation that supports post-silicon validation and debug, volume test and diagnosis, as well as in-field system monitoring and maintenance. Reconfigurable scan architectures, as allowed by the novel IEEE Std 1149.1-2013 (JTAG) and IEEE Std 1687- 2014 (IJTAG), emerge as a scalable mechanism for access to such on-chip instruments. While the on-chip instrumentation is crucial for meeting quality, dependability, and time-to-market goals, it is prone to abuse and threatens system safety and security. A secure access management method is mandatory to assure that critical instruments be accessible to authorized entities only. This work presents a novel protection method for fine-grained access management in complex reconfigurable scan networks based on a challenge-response authentication protocol. The target scan network is extended with an authorization instrument and Secure Segment Insertion Bits (S²IB) that together control the accessibility of individual instruments. To the best of the authors’ knowledge, this is the first fine-grained access management scheme that scales well with the number of protected instruments and offers a high level of security. Compared with recent stateof- the-art techniques, this scheme is more favorable with respect to implementation cost, performance overhead, and provided security level.

Zusammenfassung

Many EDA tasks like test set characterization or the precise estimation of power consumption, power droop and temperature development, require a very large number of time-aware gate-level logic simulations. Until now, such characterizations have been feasible only for rather small designs or with reduced precision due to the high computational demands. The new simulation system presented here is able to accelerate such tasks by more than two orders of magnitude and provides for the first time fast and comprehensive timing simulations for industrial-sized designs. Hazards, pulse-filtering, and pin-to-pin delay are supported for the first time in a GPGPU accelerated simulator, and the system can easily be extended to even more realistic delay models and further applications. A sophisticated mapping with efficient memory utilization and access patterns as well as minimal synchronizations and control flow divergence is able to use the full potential of GPGPU architectures. To provide such a mapping, we combine for the first time the versatility of event-based timing simulation and multidimensional parallelism used in GPU-based gate-level simulators. The result is a throughput-optimized timing simulation algorithm, which runs many simulation instances in parallel and at the same time fully exploits gate-parallelism within the circuit.

Zusammenfassung

Nanoscale technologies are increasingly susceptible to aging processes such as Negative-Bias Temperature Instability (NBTI) which undermine the reliability of VLSI systems. Existing monitoring techniques can detect the violation of safety margins and hence make the prediction of an imminent failure possible. However, since such techniques can only detect measurable degradation effects which appear after a relatively long period of system operation, they are not well suited to early aging prediction and proactive aging alleviation. This work presents a novel method for the monitoring of NBTI-induced degradation rate in digital circuits. It enables the timely adoption of proper mitigation techniques that reduce the impact of aging. The developed method employs machine learning techniques to find a small set of so called Representative Critical Gates (RCG), the workload of which is correlated with the degradation of the entire circuit. The workload of RCGs is observed in hardware using so called workload monitors. The output of the workload monitors is evaluated on-line to predict system degradation experienced within a configurable (short) period of time, e.g. a fraction of a second. Experimental results show that the developed monitors predict the degradation rate with an average error of only 1.6% at 4.2% area overhead.

Zusammenfassung

The simulation of delay faults is an essential task in design validation and reliability assessment of circuits. Due to the high sensitivity of current nano-scale designs against smallest delay deviations, small delay faults recently became the focus of test research. Because of the subtle delay impact, traditional fault simulation approaches based on abstract timing models are not sufficient for representing small delay faults. Hence, timing accurate simulation approaches have to be utilized, which quickly become inapplicable for larger designs due to high computational requirements. In this work we present a waveform-accurate approach for fast high-throughput small delay fault simulation on Graphics Processing Units (GPUs). By exploiting parallelism from gates, faults and patterns, the proposed approach enables accurate exhaustive small delay fault simulation even for multi-million gate designs without fault dropping for the first time.

Zusammenfassung

Die Simulation kleiner Verzögerungsfehler ist ein wichtiger Bestandteil der Validierung nano-elektronischer Schaltungen. Prozessvariationen während der Herstellung haben großen Einfluss auf die Erkennung dieser Fehler und müssen bei der Simulation berücksichtigt werden. Die zeitgenaue Simulation von Verzögerungsfehlern ist verglichen mit traditioneller Logiksimulation oder statischer Zeitanalyse sehr aufwändig und die Rechenkomplexität steigt durch die Berücksichtigung von Variationen zusätzlich an. In dieser Arbeit wird ein hochparalleles Verfahren vorgestellt, welches Grafikprozessoren zur beschleunigten parallelen Simulation kleiner Verzögerungsfehler unter Variation anwendet. Das Verfahren berechnet akkurate Signalverläufe in der Schaltung und ermöglicht die Bestimmung einer Monte-Carlo-basierten statistischen Fehlererfassung für industrielle Schaltkreise unter zufälliger sowie systematischer Variation.

Zusammenfassung

Efficient access to on-chip instrumentation is a key requirement for post-silicon validation, test, debug, bringup, and diagnosis. Reconfigurable scan networks, as proposed by e.g. IEEE P1687 and IEEE Std 1149.1-2013, emerge as an effective and affordable means to cope with the increasing complexity of on-chip infrastructure. Reconfigurable scan networks are often hierarchical and may have complex structural and functional dependencies. Common approaches for scan verification based on static structural analysis and functional simulation are not sufficient to ensure correct operation of these types of architectures. To access an instrument in a reconfigurable scan network, a scan-in bit sequence must be generated according to the current state and structure of the network. Due to sequential and combinational dependencies, the access pattern generation process (pattern retargeting) poses a complex decision and optimization problem. This article presents the first generalized formal model that considers structural and functional dependencies of reconfigurable scan networks and is directly applicable to P1687-based and 1149.1-2013-based scan architectures. This model enables efficient formal verification of complex scan networks, as well as automatic generation of access patterns. The proposed pattern generation method supports concurrent access to multiple target scan registers (access merging) and generates short scan-in sequences.

Zusammenfassung

Scan infrastructures based on IEEE Std. 1149.1 (JTAG), 1500 (SECT), and P1687 (IJTAG) provide a cost-effective access mechanism for test, reconfiguration, and debugging purposes. The improved accessibility of on-chip instruments, however, poses a serious threat to system safety and security. While state-of-theart protection methods for scan architectures compliant with JTAG and SECT are very effective, most of these techniques face scalability issues in reconfigurable scan networks allowed by the upcoming IJTAG standard. This paper describes a scalable solution for multilevel access management in reconfigurable scan networks. The access to protected instruments is restricted locally at the interface to the network. The access restriction is realized by a sequence filter that allows only a precomputed set of scan-in access sequences. This approach does not require any modification of the scan architecture and causes no access time penalty. Therefore, it is well suited for core-based designs with hard macros and 3D integrated circuits. Experimental results for complex reconfigurable scan networks show that the area overhead depends primarily on the number of allowed accesses, and is marginal even if this number exceeds the count of registers in the network.

Zusammenfassung

Structural tests provide high defect coverage by considering the low-level circuit details. Functional test provides a faster test with reduced test patterns and does not imply additional hardware overhead. However, it lacks a quantitative measure of structural fault coverage. This paper fills this gap by presenting a satisfiability based method to generate functional test patterns while considering structural faults. The method targets NoC switches and links, and it is independent of the switch structure and the network topology. It can be applied for any structural fault type as it relies on a generalized structural fault model.

Zusammenfassung

This survey introduces into the common practices, current challenges and advanced techniques of high quality system level test and diagnosis. Specialized techniques and industrial standards of testing complex boards are introduced. The reuse for system test of design for test structures and test data developed at chip level is discussed, including the limitations and research challenges. Structural test methods have to be complemented by functional test methods. State-of-the-art and leading edge research for functional testing will be covered.

Zusammenfassung

Gate-level timing simulation of combinational CMOS circuits is the foundation of a whole array of important EDA tools such as timing analysis and power-estimation, but the demand for higher simulation accuracy drastically increases the runtime complexity of the algorithms. Data-parallel accelerators such as Graphics Processing Units (GPUs) provide vast amounts of computing performance to tackle this problem, but require careful attention to control-flow and memory access patterns. This paper proposes the novel High-Throughput Oriented Parallel Switch-level Simulator (HiTOPS), which is especially designed to take full advantage of GPUs and provides accurate time- simulation for multi-million gate designs at an unprecedented throughput. HiTOPS models timing at transistor granularity and supports all major timing-related effects found in CMOS including pattern-dependent delay, glitch filtering and transition ramps, while achieving speedups of up to two orders of magnitude compared to traditional gate-level simulators.

Zusammenfassung

Computational biology contributes important solutions for major biological challenges. Unfortunately, most applications in computational biology are highly computeintensive and associated with extensive computing times. Biological problems of interest are often not treatable with traditional simulation models on conventional multi-core CPU systems. This interdisciplinary work introduces a new multi-timescale simulation model for apoptotic receptor-clustering and a new parallel evaluation algorithm that exploits the computational performance of heterogeneous CPU-GPU computing systems. For this purpose, the different dynamics involved in receptor-clustering are separated and simulated on two timescales. Additionally, the time step sizes are adaptively refined on each timescale independently. This new approach improves the simulation performance significantly and reduces computing times from months to hours for observation times of several seconds.

Zusammenfassung

Test generation algorithms based on standard n-valued logic algebras are pessimistic in presence of unknown (X) values, overestimate the number of signals with X-values and underestimate fault coverage. Recently, an ATPG algorithm based on quantified Boolean formula (QBF) has been presented, which is accurate in presence of X-values but has limits with respect to runtime, scalability and robustness. In this paper, we consider ATPG based on restricted symbolic logic (RSL) and demonstrate its potential. We introduce a complete RSL ATPG exploiting the full potential of RSL in ATPG. Experimental results demonstrate that RSL ATPG significantly increases fault coverage over classical algorithms and provides results very close to the accurate QBF-based algorithm. An optimized version of RSL ATPG (together with accurate fault simulation) is up to 618x faster than the QBF-based solution, more scalable and more robust.

Zusammenfassung

Small delay faults may be an indicator of a reliability threat, even if they do not affect the system functionality yet. In recent years, Faster-than-at-Speed-Test (FAST) has become a feasible method to detect faults, which are hidden by the timing slack or by long critical paths in the combinational logic. FAST poses severe challenges to the automatic test equipment with respect to timing, performance, and resolution. In this paper, it is shown how logic built-in self-test (BIST) or embedded deterministic test can be used for an efficient FAST application. Running BIST just at a higher frequency is not an option, as outputs of long paths will receive undefined values due to set time violations and destroy the content of the signature registers. Instead, for a given test pattern sequence, faults are classified according to the optimal detection frequency. For each class, a MISR-based compaction scheme is adapted, such that the critical bits to be observed can be determined by algebraic computations. Experiments show that rather a small number of inter-mediate signatures have to be evaluated to observe a large fraction of hidden delay faults testable by the given test sequence.

Zusammenfassung

With increasing transient error rates, distinguishing intermittent and transient faults is especially challenging. In addition to particle strikes relatively high transient error rates are observed in architectures for opportunistic computing and in technologies under high variations. This paper presents a method to classify faults into permanent, intermittent and transient faults based on some intermediate signatures during embedded test or built-in self-test. Permanent faults are easily determined by repeating test sessions. Intermittent and transient faults can be identified by the amount of failing test sessions in many cases. For the remaining faults, a Bayesian classification technique has been developed which is applicable to large digital circuits. The combination of these methods is able to identify intermittent faults with a probability of more than 98 %.

Zusammenfassung

As the technology shrinks, nonfunctional properties (NFPs) such as reliability, vulnerability, power consumption, or heat dissipation become as important as system functionality. As NFPs often influence each other, depend on the application and workload of a system, and exhibit nonlinear behavior, NFP simulation over long periods of system operation is computationally expensive, if feasible at all. This article presents a piecewise evaluation method for efficient NFP simulation. Simulation time is divided into intervals called evaluation windows, within which the NFP models are partially linearized. High-speed functional system simulation is achieved by parallel execution of models at different levels of abstraction. A trade-off between simulation speed and accuracy is met by adjusting the size of the evaluation window. As an example, the piecewise evaluation technique is applied to analyze aging caused by two mechanisms, namely Negative Bias Temperature Instability (NBTI) and Hot Carrier Injection (HCI), in order to identify reliability hotspots. Experiments show that the proposed technique yields considerable simulation speedup at a marginal loss of accuracy.

Zusammenfassung

To cope with the problems of technology scaling, a robust design has become desirable. Self-checking circuits combined with rollback or repair strategies can provide a low cost solution for many applications. However, standard synthesis procedures may violate design constraints or lead to sub-optimal designs. The SAT-based strategies for the verification and synthesis of self-checking circuits presented in this paper can provide efficient solutions.

Zusammenfassung

This paper introduces a hybrid method to synthesize area-efficient fault-secure NoC switches to detect all errors resulting from any single-point combinational or transition fault in switches and interconnect links. Firstly, the structural faults that are always detectable by data encoding at flit-level are identified. Next, the fault-secure structure is constructed with minimized area such that errors caused by the remaining faults are detected under any given input vector. The experimental evaluation shows significant area savings compared to conventional fault-secure schemes. In addition, the resulting structure can be reused for test compaction. This reduces the amount of test response data and test time without loss of fault coverage or diagnostic resolution.

Zusammenfassung

Graphics processing units (GPUs) enable large-scale scientific applications and simulations on the desktop. To allow scientific computing on GPUs with high performance and reliability requirements, the application of software-based fault tolerance is attractive. Algorithm-Based Fault Tolerance (ABFT) protects important scientific operations like matrix multiplications. However, the application to floating-point operations necessitates the runtime classification of errors into inevitable rounding errors, allowed compute errors in the magnitude of such rounding errors, and into critical errors that are larger than those and not tolerable. Hence, an ABFT scheme needs suitable rounding error bounds to detect errors reliably. The determination of such error bounds is a highly challenging task, especially since it has to be integrated tightly into the algorithm and executed autonomously with low performance overhead. In this work, A-ABFT for matrix multiplications on GPUs is introduced, which is a new, parallel ABFT scheme that determines rounding error bounds autonomously at runtime with low performance overhead and high error coverage.

Zusammenfassung

This paper presents a new hybrid fault-tolerant architecture for robustness improvement of digital CMOS circuits and systems. It targets all kinds of errors in combinational part of logic circuits and thus, can be combined with advanced SEU protection techniques for sequential elements while reducing the power consumption. The proposed architecture combines different types of redundancies: information redundancy for error detection, temporal redundancy for soft error correction and hardware redundancy for hard error correction. Moreover, it uses a pseudo-dynamic comparator for SET and timing errors detection. Besides, the proposed method also aims to reduce power consumption of fault-tolerant architectures while keeping a comparable area overhead compared to existing solutions. Results on the largest ISCAS'85 and ITC'99 benchmark circuits show that our approach has an area cost of about 3 % to 6 % with a power consumption saving of about 33 % compared to TMR architectures.

Zusammenfassung

Soft errors are a reliability threat for reconfigurable systems implemented with SRAM-based FPGAs. They can be handled through fault tolerance techniques like scrubbing and modular redundancy. However, selecting these techniques statically at design or compile time tends to be pessimistic and prohibits optimal adaptation to changing soft error rate at runtime. We present the GUARD method which allows for autonomous runtime reliability management in reconfigurable architectures: Based on the error rate observed during runtime, the runtime system dynamically determines whether a computation should be executed by a hardened processor, or whether it should be accelerated by inherently less reliable reconfigurable hardware which can trade-off performance and reliability. GUARD is the first runtime system for reconfigurable architectures that guarantees a target reliability while optimizing the performance. This allows applications to dynamically chose the desired degree of reliability. Compared to related work with statically optimized fault tolerance techniques, GUARD provides up to 68.3% higher performance at the same target reliability.

Zusammenfassung

The constantly growing amount of semiconductors in automotive systems increases the number of possible defect mechanisms, and therefore raises also the effort to maintain a sufficient level of quality and reliability. A promising solution to this problem is the on-line application of structural tests in key components, typically ECUs. In this work, an approach for the optimized integration of both Software-Based Self-Tests (SBST) and Built-In Self-Tests (BIST) into E/E architectures is presented. The approach integrates the execution of the tests non-intrusively, i. e., it (a) does not affect functional applications and (b) does not require costly changes in the communication schedules or additional communication overhead. Via design space exploration, optimized implementations with respect to multiple conflicting objectives, i. e., monetary costs, safety, test quality, and required execution time are derived.