An efficient, low-cost routing architecture for spiking neural network hardware implementations

Luo, Yuling, Wan, Lei, Harkin, Jim and Cao, Yi (2018) An efficient, low-cost routing architecture for spiking neural network hardware implementations Neural Processing Letters.

Text An efficient, low-cost routing architecture for spiking neural network hardware implementations.pdf - Accepted version Manuscript Restricted to Repository staff only until 3 February 2019. Download (590kB)

Abstract

The basic processing units in brain are neurons and synapses that are interconnected in a complex pattern and show many surprised information processing capabilities. The researchers attempt to mimic this efficiency and build artificial neural systems in hardware device to emulate the key information processing principles of the brain. However, the neural network hardware system has a challenge of interconnecting neurons and synapses efficiently. An efficient, low-cost routing architecture (ELRA) is proposed in this paper to provide a communication infrastructure for the hardware spiking neuron networks (SNN). A dynamic traffic arbitration strategy is employed in ELRA, where the traffic status weights of input ports are calculated in real-time according to the channel traffic statuses and the port with the largest traffic status weight is given a high priority to forward packets. This strategy enables the router to serve congested ports preferentially, which can balance the overall network traffic loads. Experimental results show the feasibility of ELRA under various traffic scenarios, and the hardware synthesis result using SAED 90nm technology demonstrates it has a low hardware area overhead which maintains scalability for large-scale SNN hardware implementations.

Item Type:	Article
Divisions :	Faculty of Arts and Social Sciences > Surrey Business School
Authors :	Name Email ORCID Luo, Yuling Wan, Lei Harkin, Jim Cao, Yi yi.cao@surrey.ac.uk
Date :	9 February 2018
Identification Number :	10.1007/s11063-018-9797-5
Copyright Disclaimer :	© Springer Science+Business Media, LLC, part of Springer Nature 2018. This is a post-peer-review, pre-copyedit version of an article published in Neural Processing Letters. The final authenticated version is available online at: https://doi.org/10.1007/s11063-018-9797-5
Uncontrolled Keywords :	Spiking neural networks; Networks-on-Chip; Routing arbitration
Related URLs :	Publisher
Depositing User :	Clive Harris
Date Deposited :	05 Feb 2018 14:48
Last Modified :	13 Mar 2018 15:54
URI:	http://epubs.surrey.ac.uk/id/eprint/845743

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