A New Stochastic Computing Multiplier with Application to Deep Convolutional Neural Networks

Abstract

Stochastic computing (SC) allows for extremely low cost and low power implementations of common arithmetic operations. However inherent random fluctuation error and long latency of SC lead to the degradation of accuracy and energy efficiency when applied to convolutional neural networks (CNNs). In this paper we address the two critical problems of SC-based CNNs, by proposing a novel SC multiply algorithm and its vector extension, SC-MVM (Matrix-Vector Multiplier), under which one SC multiply takes just a few cycles, generates much more accurate results, and can be realized with significantly less cost, as compared to the conventional SC method. Our experimental results using CNNs designed for MNIST and CIFAR-10 datasets demonstrate that not only is our SC-based CNN more accurate and 40X~490X more energy-efficient in computation than the conventional SC-based ones, but ours can also achieve lower area-delay product and lower energy compared with bitwidth-optimized fixed-point implementations of the same accuracy.