SENIN: An energy-efficient sparse neuromorphic system with on-chip learning

Myung Hoon Choi, Seungkyu Choi, Jaehyeong Sim, Lee Sup Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Applying highly accurate neural networks to mobile devices encounters energy problems in battery-limited mobile environments. To resolve these problems, neuromorphic hardware solutions that enable event-driven operation have been proposed. In this work, we present a novel sparse neuromorphic system that implements an E-I Net algorithm to further improve energy efficiency. We introduce a neuron clock-gating technique that significantly reduces energy consumption by predicting future neuron spike activity without any loss of accuracy. We also propose synaptic pruning to save additional energy with minimal impact on classification accuracy. For fast adaptation to a changing environment, a learning algorithm is implemented in the proposed system. Compared to prior studies, our experimental results illustrate that the proposed system achieves 5.3×-11.4× energy efficiency improvement with comparable accuracy.

Original languageEnglish
Title of host publicationISLPED 2017 - IEEE/ACM International Symposium on Low Power Electronics and Design
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060238
DOIs
StatePublished - 11 Aug 2017
Event22nd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2017 - Taipei, Taiwan, Province of China
Duration: 24 Jul 201726 Jul 2017

Publication series

NameProceedings of the International Symposium on Low Power Electronics and Design
ISSN (Print)1533-4678

Conference

Conference22nd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2017
Country/TerritoryTaiwan, Province of China
CityTaipei
Period24/07/1726/07/17

Keywords

  • Neural Network
  • Neuromorphic Computing
  • Sparse Spike

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