Recent Progress on Memristive Convolutional Neural Networks for Edge Intelligence

Abstract

Recently, due to the development of big data and computer technology, artificial intelligence (AI) has received extensive attention and made great progress. Edge intelligence pushes the computing center of AI from the cloud to individual users, making AI closer to life, but at the same time puts forward higher requirements for the realization of hardware, especially for edge acceleration. Taking convolutional neural networks (CNNs) as an example, which show excellent problem‐solving capabilities in different fields of academia and industry, it still faces issues of enormous computing volume and complex mapping architecture. Based on the computing‐in‐memory property and parallel multiply accumulate (MAC) operations of the emerging nonvolatile memristor arrays, herein the recent research progress of the edge intelligence memristive convolution accelerator is summarized. Furthermore, aiming at improving memristive convolutional accelerators, two potential optimization schemes are also discussed: The compression methods represented by quantization show great potential for static image processing, and the combination of a CNN with a long short‐term memory (LSTM) neural network makes up for the CNN's shortcomings of dynamic target processing. Finally, the future challenges and opportunities of edge intelligence accelerators based on memristor arrays are also discussed.