A Study on the Low-Power Operation of the Spike Neural Network Using the Sensory Adaptation Method

Mingi Jeon, Taewook Kang, Jae-Jin Lee and Woojoo Lee ()
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Mingi Jeon: School of Electrical and Electronics Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
Taewook Kang: Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
Jae-Jin Lee: Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
Woojoo Lee: School of Electrical and Electronics Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea

Mathematics, 2022, vol. 10, issue 22, 1-19

Abstract: Motivated by the idea that there should be a close relationship between biological significance and low power driving of spike neural networks (SNNs), this paper aims to focus on spike-frequency adaptation, which deviates significantly from existing biological meaningfulness, and develop a new spike-frequency adaptation with more biological characteristics. As a result, this paper proposes the s e n s o r y a d a p t a t i o n method that reflects the mechanisms of the human sensory organs, and studies network architectures and neuron models for the proposed method. Next, this paper introduces a dedicated SNN simulator that can selectively apply the conventional spike-frequency adaptation and the proposed method, and provides the results of functional verification and effectiveness evaluation of the proposed method. Through intensive simulation, this paper reveals that the proposed method can produce a level of training and testing performance similar to the conventional method while significantly reducing the number of spikes to 32.66% and 45.63%, respectively. Furthermore, this paper contributes to SNN research by showing an example based on in-depth analysis that embedding biological meaning in SNNs may be closely related to the low-power driving characteristics of SNNs.

Keywords: artificial neural networks; spiking neural networks; neuromorphic; frequency adaptation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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