Approximate Analytical Model Evaluating Digital Systems Compliance with Automotive Standards

This paper revisits the existing Clustering Probabilistic Binomial Reliability (CPBR) approximate analytical model based on circuit partitioning and signal transfer matrices. It demonstrates the model's relevance in assessing the correctness rate of logic gate netlists, in accordance with the ISO26262 road safety standard. The paper also highlights the model's applicability to typical industrial circuit netlists. While prior work established the CPBR model's ability to efficiently propagate signal error rates to circuit outputs with reasonable accuracy, no prior work has comprehensively compared its correctness rates to those of its reference Probabilistic Binomial Reliability (PBR) model. The novel CPBR implementation used in this paper enables, for the first time, a detailed accuracy comparison between the partitioning model and its reference across multiple circuits of the ISCAS’85 benchmark suite. Additionally, we provide an in-depth analysis of how this approximate method impacts the Automotive Safety Integrity Level (ASIL) for these circuits, identifying the optimal ASIL target for its application. Finally, we showcase the practical relevance of our approach by successfully applying it to the complex peripheral netlists of an industrial test-chip embedding a RISC-V core, demonstrating both its scalability and effectiveness in real-world scenarios.