Fast on-device adaptation for spiking neural networks via online-within-online meta-learning

Bleema Rosenfeld, Bipin Rajendran, Osvaldo Simeone

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

Abstract

Spiking Neural Networks (SNNs) have recently gained popularity as machine learning models for on-device edge intelligence for applications such as mobile healthcare management and natural language processing due to their low power profile. In such highly personalized use cases, it is important for the model to be able to adapt to the unique features of an individual with only a minimal amount of training data. Meta-learning has been proposed as a way to train models that are geared towards quick adaptation to new tasks. The few existing meta-learning solutions for SNNs operate offline and require some form of backpropagation that is incompatible with the current neuromorphic edge-devices. In this paper, we propose an online-within-online meta-learning rule for SNNs termed OWOML-SNN, that enables lifelong learning on a stream of tasks, and relies on local, backprop-free, nested updates.

Original languageEnglish (US)
Title of host publication2021 IEEE Data Science and Learning Workshop, DSLW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665428255
DOIs
StatePublished - Jun 5 2021
Event2021 IEEE Data Science and Learning Workshop, DSLW 2021 - Toronto, Canada
Duration: Jun 5 2021Jun 6 2021

Publication series

Name2021 IEEE Data Science and Learning Workshop, DSLW 2021

Conference

Conference2021 IEEE Data Science and Learning Workshop, DSLW 2021
Country/TerritoryCanada
CityToronto
Period6/5/216/6/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Education

Keywords

  • Meta-learning
  • Online learning
  • Spiking neural network

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