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.
Original language | English |
---|---|
Article number | 43 |
Journal | Computers |
Volume | 7 |
Issue number | 3 |
Early online date | 15 Aug 2018 |
DOIs | |
Publication status | Published - 30 Sept 2018 |
Keywords
- Architecture
- C. Elegans
- FPGA
- Neural-network
- Neuromorphic
- Reconfigurable
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Networks and Communications
Fingerprint
Dive into the research topics of 'An analytical comparison of locally-connected reconfigurable neural network architectures using a C. elegans locomotive model'. Together they form a unique fingerprint.Profiles
-
Benjamin Metcalfe, FRSA
- Department of Electronic & Electrical Engineering - Head of Department
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Bioengineering & Biomedical Technologies (CBio)
- Bath Institute for the Augmented Human
- IAAPS: Propulsion and Mobility
Person: Research & Teaching, Core staff, Affiliate staff
-
Peter Wilson
- Department of Electronic & Electrical Engineering - Professor, Professor (Visiting )
Person: Research & Teaching, Honorary / Visiting Staff