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A memory-efficient routing method for large-scale spiking neural networks


Moradi, Saber; Imam, Nabil; Manohar, Rajit; Indiveri, Giacomo (2013). A memory-efficient routing method for large-scale spiking neural networks. In: European Conference on Circuit Theory and Design 2013 (ECCTD), Dresden, Germany, 8 September 2013 - 12 September 2013, 1-4.

Abstract

Progress in VLSI technologies is enabling the integration of large numbers of spiking neural network processing modules into compact systems. Asynchronous routing circuits are typically employed to efficiently interface these modules, and configurable memory is usually used to implement synaptic connectivity among them. However, supporting arbitrary network connectivity with conventional routing methods would require prohibitively large memory resources. We propose a two stage routing scheme which minimizes the memory requirements needed to implement scalable and reconfigurable spiking neural networks with bounded connectivity. Our routing methodology trades off network configuration flexibility for routing memory demands and is optimized for the most common and anatomically realistic neural network topologies. We describe and analyze our routing method and present a case study with a large neural network.

Progress in VLSI technologies is enabling the integration of large numbers of spiking neural network processing modules into compact systems. Asynchronous routing circuits are typically employed to efficiently interface these modules, and configurable memory is usually used to implement synaptic connectivity among them. However, supporting arbitrary network connectivity with conventional routing methods would require prohibitively large memory resources. We propose a two stage routing scheme which minimizes the memory requirements needed to implement scalable and reconfigurable spiking neural networks with bounded connectivity. Our routing methodology trades off network configuration flexibility for routing memory demands and is optimized for the most common and anatomically realistic neural network topologies. We describe and analyze our routing method and present a case study with a large neural network.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:12 September 2013
Deposited On:12 Feb 2014 17:00
Last Modified:05 Apr 2016 17:33
Publisher:Proceedings of the European Conference on Circuit Theory and Design 2013 (ECCTD)
Series Name:21st European Conference on Circuit Theory and Design
Number of Pages:4
Publisher DOI:https://doi.org/10.1109/ECCTD.2013.6662203
Permanent URL: https://doi.org/10.5167/uzh-91167

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