Research Article Open Access

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

Sunny Bodiwala1 and Nirali Nanavati2
  • 1 Gujarat Technological University, India
  • 2 Sarvajanik College of Engineering and Technology, India
Journal of Computer Science
Volume 16 No. 11, 2020, 1570-1584

DOI: https://doi.org/10.3844/jcssp.2020.1570.1584

Submitted On: 27 August 2020 Published On: 17 November 2020

How to Cite: Bodiwala, S. & Nanavati, N. (2020). Efficient Implementation of Stochastic Computing Based Deep Neural Network on Low Cost Hardware with Saturation Arithmetic. Journal of Computer Science, 16(11), 1570-1584. https://doi.org/10.3844/jcssp.2020.1570.1584

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.

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Keywords

  • Deep Neural Network
  • FPGA
  • Accelerator
  • Optimization
  • Stochastic Computing
  • Custom Computing