"Fast on-device adaptation for spiking neural networks via online-withi" by Bleema Rosenfeld, Bipin Rajendran et al.
 

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

Document Type

Conference Proceeding

Publication Date

6-5-2021

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.

Identifier

85115418988 (Scopus)

ISBN

[9781665428255]

Publication Title

2021 IEEE Data Science and Learning Workshop Dslw 2021

External Full Text Location

https://doi.org/10.1109/DSLW51110.2021.9523405

Grant

1525629

Fund Ref

National Science Foundation

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