PARSIVAL

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.