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ACCURATE, ENERGY-EFFICIENT CLASSIFICATION WITH SPIKING RANDOM NEURAL NETWORK

Published online by Cambridge University Press:  21 May 2019

Khaled F. Hussain ,
Mohamed Yousef Bassyouni Open the ORCID record for Mohamed Yousef Bassyouni [Opens in a new window]  and
Erol Gelenbe
Khaled F. Hussain
Affiliation:
Computer Science Department, Faculty of Computers and Information, Assiut University, Asyut71515, Egypt E-mail: [email protected]; [email protected]
Mohamed Yousef Bassyouni
Affiliation:
Computer Science Department, Faculty of Computers and Information, Assiut University, Asyut71515, Egypt E-mail: [email protected]; [email protected]
Erol Gelenbe
Affiliation:
Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College London, SW7 2BT, UK E-mail: [email protected]
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Abstract

Artificial Neural Networks (ANNs)-based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large-scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the mean-time, Neuromorphic Computing platforms have recently achieved remarkable performance running the bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network, a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of being a spiking neural network. This is demonstrated on a number of real-world classification datasets.

Keywords

artificial neural networkneuromorphic computingrandom neural networkspiking neural networks
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 35 , Special Issue 1: Learning, Optimization, and Theory of G-Networks , January 2021 , pp. 51 - 61
Copyright
Copyright © Cambridge University Press 2019

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