Probabilistic model checking in practice: case studies with PRISM
ACM SIGMETRICS Performance Evaluation Review
Toward Hardware-Redundant, Fault-Tolerant Logic for Nanoelectronics
IEEE Design & Test
Probabilistic transfer matrices in symbolic reliability analysis of logic circuits
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Quantitative Analysis With the Probabilistic Model Checker PRISM
Electronic Notes in Theoretical Computer Science (ENTCS)
Weighted probabilistic equivalence preserves ω-regular properties
MMB'12/DFT'12 Proceedings of the 16th international GI/ITG conference on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance
Variable probabilistic abstraction refinement
ATVA'12 Proceedings of the 10th international conference on Automated Technology for Verification and Analysis
Reliable on-chip systems in the nano-era: lessons learnt and future trends
Proceedings of the 50th Annual Design Automation Conference
Formal performance analysis for faulty MIMO hardware
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Probabilistic-model checking is a formal verification technique for analyzing the reliability and performance of systems exhibiting stochastic behavior. In this paper, we demonstrate the applicability of this approach and, in particular, the probabilistic-model-checking tool PRISM to the evaluation of reliability and redundancy of defect-tolerant systems in the field of computer-aided design. We illustrate the technique with an example due to von Neumann, namely NAND multiplexing. We show how, having constructed a model of a defect-tolerant system incorporating probabilistic assumptions about its defects, it is straightforward to compute a range of reliability measures and investigate how they are affected by slight variations in the behavior of the system. This allows a designer to evaluate, for example, the tradeoff between redundancy and reliability in the design. We also highlight errors in analytically computed reliability bounds, recently published for the same case study.