A General Framework to Map Neural Networks onto Neuromorphic Processor

Haowen Fang, Amar Shrestha, Ziyi Zhao, Yanzhi Wang, Qinru Qiu

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

1 Scopus citations

Abstract

Bio-inspired neuromorphic hardware is an emerging computing architecture, which features highly parallel and distributed computing elements similar to the functionality of human brain. Recent study shows that neuromorphic hardware can achieve state-of-the-art performance in various cognitive tasks. However, limitations in fabrication technology has led to limitations in fan-in, fan-out, memory capacity, connectivity etc., making neuromorphic chips difficult to program. Neural networks have to satisfy specific constraints in order to be mapped to hardware, which not only requires developers to have knowledge of specific hardware, but also makes training difficult. We proposed a general framework to address above issues. It consists of a workflow to convert an existing neural network to satisfy the hardware constrains while minimizing the error caused by conversion, algorithms to increase hardware resource utilization and minimize on-chip communication cost are also proposed and evaluated. The experimental results show that the framework reduces conversion error to 0.67%, and reduces 53% of communication latency.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th International Symposium on Quality Electronic Design, ISQED 2019
PublisherIEEE Computer Society
Pages20-25
Number of pages6
ISBN (Electronic)9781728103921
DOIs
StatePublished - Apr 23 2019
Event20th International Symposium on Quality Electronic Design, ISQED 2019 - Santa Clara, United States
Duration: Mar 6 2019Mar 7 2019

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
Volume2019-March
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Conference

Conference20th International Symposium on Quality Electronic Design, ISQED 2019
CountryUnited States
CitySanta Clara
Period3/6/193/7/19

Keywords

  • Neuromorphic hardware
  • spiking neural network

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

Fingerprint Dive into the research topics of 'A General Framework to Map Neural Networks onto Neuromorphic Processor'. Together they form a unique fingerprint.

  • Cite this

    Fang, H., Shrestha, A., Zhao, Z., Wang, Y., & Qiu, Q. (2019). A General Framework to Map Neural Networks onto Neuromorphic Processor. In Proceedings of the 20th International Symposium on Quality Electronic Design, ISQED 2019 (pp. 20-25). [8697495] (Proceedings - International Symposium on Quality Electronic Design, ISQED; Vol. 2019-March). IEEE Computer Society. https://doi.org/10.1109/ISQED.2019.8697495