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An Efficient, Low-Cost Routing Architecture for Spiking Neural Network Hardware Implementations

  • Yuling Luo
  • Lei Wan
  • Junxiu Liu
  • Jim Harkin
  • Yi Cao
Article

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 90 nm technology demonstrates it has a low hardware area overhead which maintains scalability for large-scale SNN hardware implementations.

Keywords

Spiking neural networks Networks-on-chip Routing arbitration 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China under Grants 61603104 and 61661008, the Guangxi Natural Science Foundation under Grants 2015GXNSFBA139256 and 2016GXNSFCA380017, the funding of Overseas 100 Talents Programme of Guangxi Higher Education, the Research Project of Guangxi University of China under Grant KY2016YB059, Guangxi Key Lab of Multi-source Information Mining and Security under Grant MIMS15-07, the Doctoral Research Foundation of Guangxi Normal University, the Research Project of Guangxi Centre of Humanities and Social Sciences—Ecological Environment Forecast and Harnessing in Ecologically Vulnerable Region of Pearl River and Xijiang Economic Zone (ZX2016030), and the Innovation Project of Guangxi Graduate Education (YCSZ2016034).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Guangxi Key Lab of Multi-Source Information Mining and Security, Faculty of Electronic EngineeringGuangxi Normal UniversityGuilinChina
  2. 2.School of Computing, Engineering and Intelligent SystemsUlster UniversityLondonderryUK
  3. 3.Department of Business Transformation and Sustainable Enterprise, Surrey Business SchoolUniversity of SurreySurreyUK

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