An event-driven neuromorphic system with biologically plausible temporal dynamics

Haowen Fang, Amar Shrestha, Ziyi Zhao, Yilan Li, Qinru Qiu

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

Abstract

Driven by the expanse of Internet of Things (IoT) and Cyber-Physical Systems (CPS), there is an increasing demand to process streams of temporal data on embedded devices with limited energy and power resources. Among all potential solutions, neuromorphic computing with spiking neural networks (SNN) that mimic the behavior of brain, have recently been placed at the forefront. Encoding information into sparse and distributed spike events enables low-power implementations, and the complex spatial temporal dynamics of synapses and neurons enable SNNs to detect temporal pattern. However, most existing hardware SNN implementations use simplified neuron and synapse models ignoring synapse dynamic, which is critical for temporal pattern detection and other applications that require temporal dynamics. To adopt a more realistic synapse model in neuromorphic platform its significant computation overhead must be addressed. In this work, we propose an FPGA-based SNN with biologically realistic neuron and synapse for temporal information processing. An encoding scheme to convert continuous real-valued information into sparse spike events is presented. The event-driven implementation of synapse dynamic model and its hardware design that is optimized to exploit the sparsity are also presented. Finally, we train the SNN on various temporal pattern-learning tasks and evaluate its performance and efficiency as compared to rate-based models and artificial neural networks on different embedded platforms. Experiments show that our work can achieve 10X speed up and 196X gains in energy efficiency compared with GPU.

Original languageEnglish (US)
Title of host publication2019 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728123509
DOIs
StatePublished - Nov 2019
Event38th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019 - Westin Westminster, United States
Duration: Nov 4 2019Nov 7 2019

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume2019-November
ISSN (Print)1092-3152

Conference

Conference38th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019
CountryUnited States
CityWestin Westminster
Period11/4/1911/7/19

Keywords

  • FPGA
  • Neuromorphic computing
  • Spiking neural network

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Fang, H., Shrestha, A., Zhao, Z., Li, Y., & Qiu, Q. (2019). An event-driven neuromorphic system with biologically plausible temporal dynamics. In 2019 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019 - Digest of Technical Papers [8942083] (IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD; Vol. 2019-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCAD45719.2019.8942083