Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic computing whereby a single logic gate can perform the arithmetic operation by exploiting the nature of probability math. SC was proposed in the 1960s when binary computing was expensive. However, presently, SC started to regain interest after the widespread of deep learning application, specifically the convolutional neural network (CNN) algorithm due to its practicality in hardware implementation. Although not all computing functions can translate to the SC domain, several useful function blocks related to the CNN algorithm had been proposed and tested by researchers. An evolution of CNN, namely, binarised neural network, had also gained attention in the edge computing due to its compactness and computing efficiency. This study reviews various SC CNN hardware implementation methodologies. Firstly, we review the fundamental concepts of SC and the circuit structure and then compare the advantages and disadvantages amongst different SC methods. Finally, we conclude the overview of SC in CNN and make suggestions for widespread implementation.

中文翻译：

---

卷积神经网络实现中的随机计算：综述

随机计算（SC）是用于确定性计算的另一种计算领域，其中单个逻辑门可以通过利用概率数学的性质来执行算术运算。SC是在1960年代提出的，当时二进制计算非常昂贵。但是，由于深度学习应用（尤其是卷积神经网络（CNN）算法）在硬件实现方面的实用性，因此目前，SC开始引起广泛的关注。尽管并非所有计算功能都可以转换为SC域，但是研究人员已经提出并测试了与CNN算法相关的几个有用功能块。CNN的发展，即二值化神经网络，由于其紧凑性和计算效率，也引起了边缘计算的关注。这项研究回顾了各种SC CNN硬件实现方法。首先，我们回顾了SC的基本概念和电路结构，然后比较了不同SC方法之间的优缺点。最后，我们总结了CNN中SC的概述，并提出了广泛实施的建议。