Hardware Oriented Computer Science - Research Projects
(DFG Project; since October 2015)
Cryptographic circuits are employed in mobile and embedded systems to protect sensitive information from unauthorized access and manipulation. Fault attacks circumvent the protection by injecting faults into the hardware implementation of the cryptographic function, thus manipulating the calculation in a controlled manner and allowing the attacker to derive protected data such as secret keys. A large number of fault attacks and counter-measures against such attacks were suggested in the last years. However, isolated techniques for each individual attack are no longer sufficient; a generic protective strategy is lacking.
The Algebraic Fault Attacks project focuses on the class of algebraic fault attacks, where the information used for cryptanalysis is represented by systems of polynomials. In order to understand the scope of such attacks and develop suitable counter-measures, techniques to conduct algebraic fault attacks will be developed.
This project aims at developing methods to realize low-cost and power-efficient hardware circuits for near-sensor computing following the Stochastic Computing paradigm. Stochastic computing provides extremely compact, error-tolerant and low-power implementations of complex functions, but at the expense of longer computation times and some degree of inaccuracy. This makes stochastic circuits (SCs) especially attractive for near-sensor computing, where the processed sensor data are inaccurate anyway and computations tend to occur infrequently. A special focus of this project will be the SC realization of neural networks (NNs) used for classification tasks, from lightweight NNs to fully-fledged convolutional NNs for deep learning.
Computer Architecture - Research Projects
An important problem in modern technology nodes in nano-electronics are early life failures, which often cause recalls of shipped products and incur high costs. An important root cause of such failures are marginal circuit structures, which pass a conventional manufacturing test, but are not able to cope with the later workload and stress in the field. Such structures can be identified on the basis of non-functional indicators, in particular by testing the timing behavior. For an effective and cost-efficient test of these indicators, the FAST project investigates novel scan designs and built-in self-test strategies for circuits, which can operate at frequencies beyond the functional specification to detect small deviations of the nominal timing behavior and thus potential early life failures.
Computer systems have reached a point where significant improvements in computational performance and energy efficiency have become very hard to achieve. The main reason is a power and efficiency wall CMOS technology is facing. Physical limitations such as high power densities and a variety of reliability degradations now enforce larger design margins which reduce efficiency.
Approximate Computing trades off precision against power, energy, storage, bandwidth or performance, and can be applied to hardware, software and algorithms. It enables much more efficient computing by providing additional, adjustable design and runtime parameters to find Pareto optimal solutions. However, its application is still rather limited and a significant extension of the scope of applications is required, including applications that are not necessarily inherently error-tolerant.
The ACCROSS project will tackle this challenge with a cross-layer approach to analysis and optimization, which considers the system stack from the application down to the hardware. At the higher levels, ACCROSS covers the analysis of applications from different computational problem classes, which will act as enablers for mainstream approximate computing. This includes the development of new methods for the analysis of approximation potentials in applications, the adaptation of existing applications to approximation and the quantification of efficiency gains. Moreover, new methods for combining suitable approximation techniques at different system layers during runtime will be provided to maximize efficiency with respect to performance and energy. New error metrics and methods for lightweight runtime monitoring of accuracy will be developed to ensure the usefulness of the targeted applications. At the lower levels, ACCROSS covers the systematic evaluation of the impact of removing design margins which will lead to approximate behavior and improved efficiency. Abstract but accurate models linking the hardware and software will be provided, enabling designers to accurately quantify the error and efficiency impact of approximation across the system stack.
RSNs were initially brought up to manage the extensive amount of instrumentation in modern systems-on-chip to facilitate cost-efficient bring-up and debug, test, diagnosis and maintenance. Recently, the reuse of RSNs at system runtime for online fault classification and fault management moved into the center of research activities. Reasons are not only the increased complexity and dependability requirements in new technologies, but also the emerging application paradigms of self-aware and autonomous systems. Especially in safety-critical applications, online test, system monitoring and fault tolerance at low cost become mandatory. For example, the standard ISO 26262 specifies critical faults to be detected within certain test intervals at runtime and allows only a maximum fault reaction time until the system has to be transferred into a safe state. The periodic test is usually structure oriented and targets stuck-at, transition and delay faults.
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Embedded Systems - Research Projects
Technology scaling makes it possible to implement systems with hundreds of processing cores, and thousands in the future, on a single chip. The communication in such systems is enabled by Networks-on-Chips (NoCs). A downside of technology scaling is the increased susceptibility to failures emerging in NoC resources during operation. Ensuring reliable operation despite such failures degrades NoC performance and may even invalidate the performance benefits expected from scaling. Thus, it is not enough to analyze performance and reliability in isolation, as usually done. Instead, we research how both aspects can be treated together using the concept of performability and its analysis with Markov reward models. In addition to developing modelling and analysis techniques, we exemplify our methodology through application to compare various NoC topologies and fault-tolerant routing algorithms. We investigate how performability develops with scaling towards larger NoCs and explore the limits of scaling by determining the break-even failure rates under which scaling can achieve net performability increase.