Spiking Neural Network Transformer for Deploying into a Deep Learning Framework
Abstract
Spiking neural network (SNNs) have been widely studied as an analysis model for human brain functioning. The energy-efficient nature of SNNs have attracted attentions of engineering researchers in deep neural networks. They sometimes need to have a tool that transforms SNNs to be executed in a deep learning framework. Due to inherent difference in their components for SNNs and deep neural networks, there are some inevitable restrictions in such transformations. This paper presents a new design and simulation environment for SNNs, which allows to build various architecture of SNNs and transforms them into computation graphs for execution. It supports several training algorithms for them. It exports their functionalities as APIs in Python with which the developers can build, train, and execute SNN models.
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Index Terms
- Spiking Neural Network Transformer for Deploying into a Deep Learning Framework
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Published In
October 2020
300 pages
ISBN:9781450380256
DOI:10.1145/3400286
Copyright © 2020 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
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Published: 25 November 2020
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RACS '20
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RACS '20: International Conference on Research in Adaptive and Convergent Systems
October 13 - 16, 2020
Gwangju, Republic of Korea
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RACS '20 Paper Acceptance Rate 42 of 148 submissions, 28%;
Overall Acceptance Rate 393 of 1,581 submissions, 25%
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