Paper Info
Title
Ameliorate Performance of Memristor-Based ANNs in Edge Computing
Abstract
Energy efficiency and delay time in the Internet of Things (IoT) system are becoming increasingly significant, especially for the emerging memristor-based crossbar arrays for smart edge computing. This article aims to find a solution for increasing energy efficiency and reducing the delay time, thereby improving the performance of ANNs in edge computing systems. The Number of Pulses Compression (NPC) method is proposed to optimize pulse distribution, energy consumption, and latency by compressing the number of pulses in every weight update step. The NPC method is implemented and verified in a memristor-based hardware simulator based on the MNIST and CIFAR-10 dataset under different circumstances of variations, failure rates, aging effects, architectures, and algorithms. The experimental results show that the NPC method can not only alleviate the uneven distribution of writing pulses but also save the writing energy of the crossbar array by 7.7--26.9 percent and reduce the writing latency by 30.0--50.0 percent. Additionally, the timing regularity of the system is enhanced by the NPC method.
Year
DOI
Venue
2021
10.1109/TC.2021.3081985
IEEE Transactions on Computers
Keywords
DocType
Volume
Artificial neural networks (ANNs),memristor,weight update,energy consumption,latency,compression,edge computing,Internet of Things (IoT)
Journal
70
Issue
ISSN
Citations 
8
0018-9340
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Zhiheng Liao122.39
Jingyan Fu202.70
Jinhui Wang38720.44