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Computers 2018, 7(3), 43; https://doi.org/10.3390/computers7030043

An Analytical Comparison of Locally-Connected Reconfigurable Neural Network Architectures Using a C. elegans Locomotive Model

Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
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Received: 28 June 2018 / Revised: 10 August 2018 / Accepted: 13 August 2018 / Published: 15 August 2018
(This article belongs to the Special Issue Reconfigurable Computing Technologies and Applications)
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Abstract

The scale of modern neural networks is growing rapidly, with direct hardware implementations providing significant speed and energy improvements over their software counterparts. However, these hardware implementations frequently assume global connectivity between neurons and thus suffer from communication bottlenecks. Such issues are not found in biological neural networks. It should therefore be possible to develop new architectures to reduce the dependence on global communications by considering the connectivity of biological networks. This paper introduces two reconfigurable locally-connected architectures for implementing biologically inspired neural networks in real time. Both proposed architectures are validated using the segmented locomotive model of the C. elegans, performing a demonstration of forwards, backwards serpentine motion and coiling behaviours. Local connectivity is discovered to offer up to a 17.5× speed improvement over hybrid systems that use combinations of local and global infrastructure. Furthermore, the concept of locality of connections is considered in more detail, highlighting the importance of dimensionality when designing neuromorphic architectures. Convolutional Neural Networks are shown to map poorly to locally connected architectures despite their apparent local structure, and both the locality and dimensionality of new neural processing systems is demonstrated as a critical component for matching the function and efficiency seen in biological networks. View Full-Text
Keywords: reconfigurable; architecture; neural-network; neuromorphic; FPGA; C. elegans reconfigurable; architecture; neural-network; neuromorphic; FPGA; C. elegans
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary materials

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  • Externally hosted supplementary file 1
    Link: https://github.com/JEGHC/CElegansSupplementary
    Description: Footage and code from the C. Elegans physics simulations, driven by the neural network running on a locally connected reconfigurable architecture.
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Graham-Harper-Cater, J.; Metcalfe, B.; Wilson, P. An Analytical Comparison of Locally-Connected Reconfigurable Neural Network Architectures Using a C. elegans Locomotive Model. Computers 2018, 7, 43.

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