A Framework for Evaluation of Debug Path Performance in SoC

Ghosh, Prokash; Sinha, Khwahish

doi:10.1109/socc52499.2021.9739317

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Currently, Multi-Processor Systems-on-Chips (MP-SoCs) in the low nanometer range (10-28 nm) are increasingly used owing to their excellent multitasking and parallel computing capabilities. MP-SoCs are used in safety-critical applications in space and aviation [1] , military systems [2] , and automotives [3] . Stress conditions, such as vibration, radiation, and temperature, in safety-critical applications are extremely severe.…”

Section: Introductionmentioning

confidence: 99%

“…To whom correspondence should be addressed. Manuscript received: 2022-08-08; revised: 2022-09-28; accepted: 2022-11-08 must withstand the operating ambient temperature of around 200 ı C [3] . Furthermore, the control electronics in aircraft must operate well in a temperature range of 55 ı C to 200 ı C. These systems require high dependability with typically a close to zero mean downtime because even a brief loss of processor control might has disastrous effects [4] .…”

Section: Introductionmentioning

confidence: 99%

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Data Fusion with Genetic Algorithm Based Lifetime Prediction for Dependable Multi-Processor System-on-Chips

Zhao,

Guo,

Zhang

2023

Tsinghua Sci. Technol.

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With the prevalence of big-data technology, intricate, nanoscale Multi-Processor System-on-Chips (MP-SoCs) have been used in various safety-critical applications. However, with no extra countermeasures taken, this widespread use of MP-SoCs can lead to an undesirable decrease in their dependability. This study presents a promising approach using a group of Embedded Instruments (EIs) inside a processor core for health monitoring.Multiple health monitoring datasets obtained from the employed EIs are sampled and collated via the implemented experiment and thereafter used for conducting its remaining useful lifetime prognostics. This enables MP-SoCs to undertake preventive self-repair, thus realizing a zero mean downtime system and ensuring improved dependability.In addition, a principal component analysis based algorithm is designed for realizing the EI data fusion. Subsequently, a genetic algorithm based degradation optimization is employed to create a lifetime prediction model with respect to the processor.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%