Stochastic computing (SC) is an old but reviving computing paradigm for its simple data path that can perform various arithmetic operations. It allows for low power implementation, which would otherwise be complex using the conventional positional binary coding. In SC, a number is encoded by a random bit stream of '0's and '1's with an equal weight for every bit. However, a long bit stream is usually required to achieve a high accuracy. This requirement inevitably incurs a long latency and high energy consumption in an SC system. In this article, we present a new type of stochastic computing that uses dynamically variable bit streams, which is, therefore, referred to as dynamic stochastic computing (DSC). In DSC, a random bit is used to encode a single value from a digital signal. A sequence of such random bits is referred to as a dynamic stochastic sequence. Using a stochastic integrator, DSC is well suited for implementing accumulation-based iterative algorithms such as numerical integration and gradient descent. The underlying mathematical models are formulated for functional analysis and error estimation. A DSC system features a higher energy efficiency than conventional computing using a fixed-point representation with a power consumption as low as conventional SC. It is potentially useful in a broad spectrum of applications including signal processing, numerical integration and machine learning.

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动态随机计算简介

随机计算 (SC) 是一种古老但正在复兴的计算范式，因为其简单的数据路径可以执行各种算术运算。它允许低功耗实现，否则使用传统的位置二进制编码会很复杂。在 SC 中，一个数字由一个随机的 0 和 1 的比特流编码，每个比特的权重相等。然而，通常需要较长的比特流才能实现高精度。这种要求不可避免地会导致 SC 系统中的长延迟和高能耗。在本文中，我们介绍了一种使用动态可变比特流的新型随机计算，因此称为动态随机计算 (DSC)。在 DSC 中，随机位用于对来自数字信号的单个值进行编码。这种随机位的序列被称为动态随机序列。使用随机积分器，DSC 非常适合实现基于累积的迭代算法，例如数值积分和梯度下降。为功能分析和误差估计制定了基础数学模型。与使用定点表示的传统计算相比，DSC 系统具有更高的能源效率，其功耗与传统 SC 一样低。它在广泛的应用中可能有用，包括信号处理、数值积分和机器学习。为功能分析和误差估计制定了基础数学模型。与使用定点表示的传统计算相比，DSC 系统具有更高的能源效率，其功耗与传统 SC 一样低。它在广泛的应用中可能有用，包括信号处理、数值积分和机器学习。为功能分析和误差估计制定了基础数学模型。与使用定点表示的传统计算相比，DSC 系统具有更高的能源效率，其功耗与传统 SC 一样低。它在广泛的应用中可能有用，包括信号处理、数值积分和机器学习。