SOLSA: Neuromorphic Spatiotemporal Online Learning for Synaptic Adaptation

Zhenhang Zhang, Jingang Jin, Haowen Fang, Qinru Qiu

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

Spiking neural networks (SNNs) are bio-plausible computing models with high energy efficiency. The temporal dynamics of neurons and synapses enable them to detect temporal patterns and generate sequences. While Backpropagation Through Time (BPTT) is traditionally used to train SNNs, it is not suitable for online learning of embedded applications due to its high computation and memory cost as well as extended latency. In this work, we present Spatiotemporal Online Learning for Synaptic Adaptation (SOLSA), which is specifically designed for online learning of SNNs composed of Leaky Integrate and Fire (LIF) neurons with exponentially decayed synapses and soft reset. The algorithm not only learns the synaptic weight but also adapts the temporal filters associated to the synapses. Compared to the BPTT algorithm, SOLSA has much lower memory requirement and achieves a more balanced temporal workload distribution. Moreover, SOLSA incorporates enhancement techniques such as scheduled weight update, early stop training and adaptive synapse filter, which speed up the convergence and enhance the learning performance. When compared to other non-BPTT based SNN learning, SOLSA demonstrates an average learning accuracy improvement of 14.2%. Furthermore, compared to BPTT, SOLSA achieves a 5% higher average learning accuracy with a 72% reduction in memory cost.

Original languageEnglish (US)
Title of host publicationASP-DAC 2024 - 29th Asia and South Pacific Design Automation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages848-853
Number of pages6
ISBN (Electronic)9798350393545
DOIs
StatePublished - 2024
Externally publishedYes
Event29th Asia and South Pacific Design Automation Conference, ASP-DAC 2024 - Incheon, Korea, Republic of
Duration: Jan 22 2024Jan 25 2024

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

Conference

Conference29th Asia and South Pacific Design Automation Conference, ASP-DAC 2024
Country/TerritoryKorea, Republic of
CityIncheon
Period1/22/241/25/24

Keywords

  • online learning
  • Spatiotemporal pattern learning
  • Spiking Neural Network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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