A Cross-Layer Analysis of Soft Error, Aging and Process Variation in Near Threshold Computing

Gebregiorgis, Anteneh; Kiamehr, Saman; Oboril, Fabian; Bishnoi, Rajendra; Tahoori, Mehdi B.

doi:10.3850/9783981537079_0270

Cited by 6 publications

(10 citation statements)

References 27 publications

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“…Uncertainty-Aware Analysis One example is a cross-level adaptive reliability prediction technique proposed in [17] that derives information from different levels of abstraction and allows to consider the simultaneous effects of process, voltage, temperature and aging variations, and soft errors on a processor. The authors of [18] propose a cross-level framework to analyze the combined impact of aging and process variation on the SER! (SER!)…”

Section: Uncertainty Considerationsmentioning

confidence: 99%

Uncertainty-Aware Compositional System-Level Reliability Analysis

Aliee

Glaß

Khosravi

et al. 2020

Dependable Embedded Systems

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Continuous technology scaling has increased the susceptibility of today’s electronic devices to manufacturing tolerances and environmental changes. The resulting uncertainty in component reliability can be only approximated or estimated at design time and might propagate to system level. Therefore, uncertainty must be considered to enable the design of robust systems. In this chapter, we propose a methodology for cross-level reliability analysis to tame the ever increasing analysis complexity of contemporary systems under the influence of uncertainties. The presented methodology combines various reliability analysis techniques across different levels of abstraction while providing an explicit modeling of uncertainties. It introduces mechanisms for (a) the composition and decomposition of the system during analysis and (b) converting analysis data between different levels of abstraction through adapters. The developed analysis techniques are integrated in an automatic electronic system-level reliability analysis tool to allow for the evaluation of reliability-increasing techniques and for DSE!. The tool thereby uses meta-heuristic algorithms for optimization and enables the comparison of system implementation candidates with objectives represented by uncertainty distributions.

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Section: Uncertainty Considerationsmentioning

confidence: 99%

Uncertainty-Aware Compositional System-Level Reliability Analysis

Aliee

Glaß

Khosravi

et al. 2020

Dependable Embedded Systems

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“…The variation-induced functional failure rate of SRAM cells is more pronounced in the nanoscale era as highly miniaturized devices are used to satisfy the density requirements [1]. SRAM cells mainly suffer from three main unreliability sources: (1) aging effects, (2) radiation-induced soft error, and (3) variation-induced functional failures [19]. The SRAM cell susceptibility to these issues increases with supply voltage downscaling.…”

Section: Functional Failure and Reliability Issues Of Ntc Memory Compmentioning

confidence: 99%

“…Atmospheric neutrons are one of the higher flux components, and their reaction has a high energy transfer. Thus, neutrons are the most likely cosmic radiations to cause soft errors [16,19]. Neutrons do not generate electron-hole pairs directly.…”

Section: Soft Error Rate In Sram Cellsmentioning

confidence: 99%

“…Analyzing failures based on a particular reliability failure mechanism is insufficient for estimating the system-level reliability as the interdependence among different failure mechanisms (such as aging, soft error, and process variation) has a considerable impact on the overall system reliability [4,19,20]. Interdependence of reliability failure mechanisms and their impact on the system Failure In-Time (FIT) rate in NTC variation) affects the overall system reliability of memory components in terms of Failure In-Time (FIT rate).…”

Section: Interdependence and Combined Effectsmentioning

confidence: 99%

“…Similarly, aging-induced SNM degradation increases the sensitivity of SRAM cell to soft errors. The problem is more pronounced when the SRAM cell is operating at NTC domain due to the wide variation extent and higher sensitivity to aging effects [19]. It has been observed that aging has ≈5% SNM and critical charge degradation at NTC while process variation-induced SNM degradation reaches as high as 60% [19].…”

Section: Interdependence and Combined Effectsmentioning

confidence: 99%

See 2 more Smart Citations

Reliability Analysis and Mitigation of Near-Threshold Voltage (NTC) Caches

Gebregiorgis

Bishnoi

Tahoori

2020

Dependable Embedded Systems

Self Cite

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Near-threshold computing (NTC) has significant role in reducing the energy consumption of modern very large-scale integrated circuits designs. However, NTC designs suffer from functional failures and performance loss. Understanding the characteristics of the functional failures and variability effects is of decisive importance in order to mitigate them, and get the utmost NTC benefits. This chapter presents a comprehensive cross-layer reliability analysis framework to assess the effect of soft error, aging, and process variation in the operation of near-threshold voltage caches. The objective is to quantify the reliability of different SRAM designs, evaluate voltage scaling potential of caches, and to find a reliability-performance optimal cache organization for an NTC microprocessor. In this chapter, the soft error rate (SER) and static noise margin (SNM) of 6T and 8T SRAM cells and their dependencies on aging and process variation are investigated by considering device, circuit, and architecture-level analysis.

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Synergistic Effect of BTI and Process Variations on the Soft Error Rate Estimation in Digital Circuits

Xiao

Liu

et al. 2022

IEEE Access

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Soft errors, aging effects and process variations have become the three most critical reliability issues for nanoscale complementary metal oxide semiconductor (CMOS) circuits. In this paper, the effects of bias temperature instability (BTI) and process variations on the threshold voltage of MOS devices are theoretically deduced, and the drift of the threshold voltage under the synergistic effect of BTI and process variations is analyzed. Based on the deduced threshold voltage drift formula under the synergistic effect, the critical charge and delay of different logic gates under different aging times in NanGate's 45 nm process are simulated and analyzed. A method based on the change in the critical charge and delay of the soft error rate (SER) calculation considering BTI effect and process variations is given, and the effectiveness of our method comprehensively demonstrated using the ISCAS85,89 benchmarks. As a result, we can observe that the effect of only considering the BTI effect on the critical charge of the circuit is smaller than that of considering both the BTI effect and process variations. The simulation results show that with increasing aging time, the synergistic effect of BTI and process variations makes the increase in the soft error rate more serious than the impact of either effect alone.

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A Cross-Layer Analysis of Soft Error, Aging and Process Variation in Near Threshold Computing

Cited by 6 publications

References 27 publications

Uncertainty-Aware Compositional System-Level Reliability Analysis

Uncertainty-Aware Compositional System-Level Reliability Analysis

Reliability Analysis and Mitigation of Near-Threshold Voltage (NTC) Caches

Synergistic Effect of BTI and Process Variations on the Soft Error Rate Estimation in Digital Circuits

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