Early Termination of STDP Learning with Spike Counts in Spiking Neural Networks

Sunghyun Choi; Jongsun Park

doi:10.1109/ISOCC50952.2020.9333061

Early Termination of STDP Learning with Spike Counts in Spiking Neural Networks

Sunghyun Choi, Jongsun Park

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Spiking neural network (SNN) is considered as one of the most promising candidates for designing neuromorphic hardware due to its low power computing capability. Since SNNs are made from imitating features of the human brain, bio-plausible spike-Timing-dependent plasticity (STDP) learning rule can be adjusted to perform unsupervised learning of SNN. In this paper, we present a spike count based early termination technique for STDP learning in SNN. To reduce redundant timesteps and calculations, spike counts of output neurons can be used to terminate the training process beforehand, thus latency and energy can be decreased. The proposed scheme reduces 50.7% of timesteps and 51.1% of total weight update during training with 0.35% accuracy drop in MNIST application.

Original language	English
Title of host publication	Proceedings - International SoC Design Conference, ISOCC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	75-76
Number of pages	2
ISBN (Electronic)	9781728183312
DOIs	https://doi.org/10.1109/ISOCC50952.2020.9333061
Publication status	Published - 2020 Oct 21
Event	17th International System-on-Chip Design Conference, ISOCC 2020 - Yeosu, Korea, Republic of Duration: 2020 Oct 21 → 2020 Oct 24

Publication series

Name	Proceedings - International SoC Design Conference, ISOCC 2020

Conference

Conference	17th International System-on-Chip Design Conference, ISOCC 2020
Country/Territory	Korea, Republic of
City	Yeosu
Period	20/10/21 → 20/10/24

Bibliographical note

Funding Information:
This work was supported by the Industrial Strategic Technology Development Program(10077445, Development of SoC technology based on Spiking Neural Cell for smart mobile and IoT Devices) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea)

Publisher Copyright:
© 2020 IEEE.

Keywords

Spiking neural network(SNN)
image classification
spike-Timing-dependent plasticity(STDP)

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering
Instrumentation
Artificial Intelligence
Hardware and Architecture

Access to Document

10.1109/ISOCC50952.2020.9333061

Cite this

Choi, S., & Park, J. (2020). Early Termination of STDP Learning with Spike Counts in Spiking Neural Networks. In Proceedings - International SoC Design Conference, ISOCC 2020 (pp. 75-76). Article 9333061 (Proceedings - International SoC Design Conference, ISOCC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISOCC50952.2020.9333061

Early Termination of STDP Learning with Spike Counts in Spiking Neural Networks. / Choi, Sunghyun; Park, Jongsun.
Proceedings - International SoC Design Conference, ISOCC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 75-76 9333061 (Proceedings - International SoC Design Conference, ISOCC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Choi, S & Park, J 2020, Early Termination of STDP Learning with Spike Counts in Spiking Neural Networks. in Proceedings - International SoC Design Conference, ISOCC 2020., 9333061, Proceedings - International SoC Design Conference, ISOCC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 75-76, 17th International System-on-Chip Design Conference, ISOCC 2020, Yeosu, Korea, Republic of, 20/10/21. https://doi.org/10.1109/ISOCC50952.2020.9333061

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Early Termination of STDP Learning with Spike Counts in Spiking Neural Networks

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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