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BASE: A Framework for Treating Errors in Stochastic Computing Systems
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DOI：10.1109/TC.2025.3626649.png)
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Abstract
En 中文
Stochastic computing (SC) is subject to many subtle, interacting, and application-driven error types often not found in conventional binary computing systems. These errors may defy standard methods of analysis and demand the use of black-box simulation to quantify system performance and accuracy. To address this issue, we propose a methodology called Bayesian Analysis of Stochastic Errors (BASE). BASE provides a comprehensive statistical basis for understanding and analyzing SC errors either in mathematical terms or in conjunction with simulation. It also introduces three new viewpoints into SC theory: bias-variance decomposition to distinguish systematic from random errors, Bayesian cost metrics to account for application dependencies, and estimator dominance to compare circuits. We demonstrate BASE’s utility via examples that reveal the intricacies of stochastic circuit errors. We also use BASE to analyze SC’s fundamental building blocks and demonstrate how to analyze circuit error using purely statistical models.
Keywords:
Approximate computing
stochastic computing
error analysis
statistical modeling
Bayesian modelling
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