An Energy-Efficient Mixed-Signal Parallel Multiply-Accumulate (MAC) Engine Based on Stochastic Computing

TL;DR

This paper introduces an energy-efficient mixed-signal MAC engine using stochastic computing, significantly reducing energy consumption for CNN operations on edge devices with a parallel architecture to address latency issues.

Contribution

The paper presents a novel mixed-signal MAC engine based on stochastic computing with a parallel architecture, achieving low energy consumption and reduced latency for CNNs.

Findings

Energy consumption of 5.03pJ per 26-input MAC operation

Parallel architecture effectively reduces latency

Suitable for edge deployment of CNNs

Abstract

Convolutional neural networks (CNN) have achieved excellent performance on various tasks, but deploying CNN to edge is constrained by the high energy consumption of convolution operation. Stochastic computing (SC) is an attractive paradigm which performs arithmetic operations with simple logic gates and low hardware cost. This paper presents an energy-efficient mixed-signal multiply-accumulate (MAC) engine based on SC. A parallel architecture is adopted in this work to solve the latency problem of SC. The simulation results show that the overall energy consumption of our design is 5.03pJ per 26-input MAC operation under 28nm CMOS technology.