Exploiting correlation in stochastic circuit design

Armin Alaghi, J. Hayes
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引用次数: 135

Abstract

Stochastic computing (SC) is a re-emerging computing paradigm which enables ultra-low power and massive parallelism in important applications like real-time image processing. It is characterized by its use of pseudo-random numbers implemented by 0-1 sequences called stochastic numbers (SNs) and interpreted as probabilities. Accuracy is usually assumed to depend on the interacting SNs being highly independent or uncorrelated in a loosely specified way. This paper introduces a new and rigorous SC correlation (SCC) measure for SNs, and shows that, contrary to intuition, correlation can be exploited as a resource in SC design. We propose a general framework for analyzing and designing combinational circuits with correlated inputs, and demonstrate that such circuits can be significantly more efficient and more accurate than traditional SC circuits. We also provide a method of analyzing stochastic sequential circuits, which tend to have inherently correlated state variables and have proven very hard to analyze.
利用随机电路设计中的相关性
随机计算(SC)是一种重新兴起的计算范式,它可以在实时图像处理等重要应用中实现超低功耗和大规模并行性。它的特点是使用由0-1序列实现的伪随机数,称为随机数(SNs),并解释为概率。准确度通常假定取决于相互作用的SNs高度独立或以松散指定的方式不相关。本文介绍了一种新的严格的SC相关性(SCC)测量方法,并表明,与直觉相反,相关性可以作为SC设计的一种资源。我们提出了一个分析和设计具有相关输入的组合电路的一般框架,并证明这种电路可以比传统的SC电路更有效和更准确。我们还提供了一种分析随机顺序电路的方法,随机顺序电路往往具有内在相关的状态变量,并且已被证明很难分析。
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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