Self-Checking Residue Number System for Low-Power Reliable Neural Network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Neural Network suffers four major issues including acceleration, power consumption, area overhead and fault tolerance. In this paper we develop a systematic approach to design a low-power, compact, fast and reliable neural network based on a redundant residue number system. Residue number systems have been applied in designing neural network except the CORDIC-based activation functions including hypertangent, logistic and softmax functions. This issue results in that the entire neural network cannot be totally self-checked and extra operations make the time, power and area reductions wasted. In our systematic approach we propose some design rules for ensuring the checking rate without loss of reductions in time, area and power consumption. From experiments on three neu-ral network with 24-bit fixed-point operations for the MNIST handwritten digit data set, 3, 4, and 5 moduli are separately employed for achieving all balanced improvements in power-saving, area-reduction, speed-acceleration and reliability pro-motion. Experimental results show that all the power, time and area can be reduced to only about one third, and the entire network in any combination of software and hardware can be self-checked in an aliasing rate of only 0.39% and TMR-correctable under the single-residue fault model.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 28th Asian Test Symposium, ATS 2019
PublisherIEEE Computer Society
Pages37-42
Number of pages6
ISBN (Electronic)9781728126951
DOIs
Publication statusPublished - 2019 Dec
Event28th IEEE Asian Test Symposium, ATS 2019 - Kolkata, India
Duration: 2019 Dec 102019 Dec 13

Publication series

NameProceedings of the Asian Test Symposium
Volume2019-December
ISSN (Print)1081-7735

Conference

Conference28th IEEE Asian Test Symposium, ATS 2019
CountryIndia
CityKolkata
Period19-12-1019-12-13

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Huang, T. C. (2019). Self-Checking Residue Number System for Low-Power Reliable Neural Network. In Proceedings - 2019 IEEE 28th Asian Test Symposium, ATS 2019 (pp. 37-42). [8949408] (Proceedings of the Asian Test Symposium; Vol. 2019-December). IEEE Computer Society. https://doi.org/10.1109/ATS47505.2019.000-3