Probabilistic diagnosis in wafer-scale systems

Arun Kumar Somani, J. Wang
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Abstract

Studies fault diagnosis based on a realistic probabilistic model for wafer-scale multiprocessor systems. In this model, an individual processor fails independently with probability p. The authors use a comparison testing approach. The testing is performed in multiple stages by the processors. They assume that different testing tasks are executed in different stages, and the coverage of each testing task is imperfect and is represented by a parameter c/sub v/. Imperfect coverage can be used to model intermittent faults where individual test may be incapable of detecting a fault. The authors present an efficient distributed self-diagnosis algorithm that probabilistically identifies faulty and fault free units. They show that our algorithm achieves very high accuracy even when the system is sparsely interconnected, and a large number of faulty units are present in the system.<>
晶圆级系统的概率诊断
研究基于现实概率模型的晶圆级多处理器系统故障诊断。在该模型中,单个处理器以概率p独立故障。作者使用比较测试方法。测试由处理器分多个阶段执行。他们假设不同的测试任务在不同的阶段执行,并且每个测试任务的覆盖率是不完善的,用参数c/sub v/表示。不完全覆盖可用于对间歇性故障进行建模,其中单个测试可能无法检测到故障。提出了一种高效的分布式自诊断算法,对故障和无故障机组进行概率识别。结果表明,即使系统是稀疏互连的,并且系统中存在大量故障单元,我们的算法也能达到非常高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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