随机计算的除法电路设计

Te-Hsuan Chen, J. Hayes
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引用次数: 37

摘要

随机计算(SC)在与伪随机比特流相关的信号概率中对数据进行编码。它可以使用标准VLSI电路进行非常小面积和低功耗的算术运算,并且具有很高的容错性。虽然加法、减法和乘法的SC实现非常简单,但除法却不是这样。已知的随机分频器采用顺序逻辑电路,其精度、收敛性等不令人满意或不太容易理解。因此,在SC设计中,除法通常是避免或近似的。我们首先回顾并深入分析了现有的随机除法设计方法。然后,我们提出了一种新的分割技术,称为CORDIV,利用输入参数之间的相关性。CORDIV不仅比以往的随机分频器成本更低,而且精度显著提高。减少面积主要是因为CORDIV需要较少的随机数字转换开销。我们提供的实验数据显示,典型的面积减少了3倍,精度提高了10倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of Division Circuits for Stochastic Computing
Stochastic computing (SC) encodes data in the signal probabilities associated with pseudo-random bit-streams. It enables very low-area and low-power arithmetic operations using standard VLSI circuits, it is also highly error-tolerant. While addition, subtraction and multiplication have extremely simple SC implementations, this is not true for division. Known stochastic dividers employ sequential logic circuits whose accuracy, convergence properties, etc., are unsatisfactory or not well under-stood. As a result, division is usually avoided or approximated in SC design. We first review and analyze in depth the existing design approaches to stochastic division. We then propose a novel division technique called CORDIV that exploits correlation between the input parameters. CORDIV not only has lower cost than previous stochastic dividers, but is also significantly more accurate. Area is reduced mainly because CORDIV requires less overhead for stochastic number conversion. We provide experimental data showing a typical 3x reduction in area and about a 10x improvement in accuracy.
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