Efficient Implementation of Stochastic Computing Based Deep Neural Network on Low Cost Hardware with Saturation Arithmetic

Abstract

This study presents an efficient and rapid implementation of Stochastic Computing (SC) based Deep Neural Network (DNN) on a low-cost hardware platform. The proposed technique uses bipolar signal encoding in stochastic computing which relatively gives low hardware footprint compared to binary computing. Thereinafter, stochastic max function is presented and subsequently used to approximate the hyperbolic tangent activation function in SC. In addition, saturation arithmetic is proposed to reduce down scaling parameters that can further affect precision in computation. In this study, we demonstrate our SC-based DNN feasibility through a hardware accelerator prototype with the AXI Stream interface on a PYNQ Z2 board which is equipped with a XILINX ZYNQ XC7Z020-1CLG400C. The validity of this study is demonstrated through a MNIST handwritten digit recognition task. The experimental result shows our SC-based DNN model can be easily deployed on the embedded devices. The SC-based accelerator with AXI Stream interface performs at 1.877 GOP/s processing throughput, achieves higher accuracy with minimum area and energy consumption, consuming only 0.61 mm² area and 1.89W power.