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Dr Themis Prodromakis

University of Southampton
Nano Group, Southampton Nanofabrication Centre
Southampton, SO17 1BJ, UK
+44 (0)23 8059 8803

Multi-Casting Mesh AER: A Scalable Assembly Approach for Reconfigurable Neuromorphic Structured AER Systems. Application to ConvNets

Authors: C. Zamarreño-Ramos, A. Linares-Barranco, T. Serrano-Gotarredona, and B. Linares-Barranco

Published by: IEEE Transactions on Biomedical Circuits and Systems

This paper presents a modular, scalable approach to assembling hierarchically structured neuromorphic Address Event Representation (AER) systems. The method consists of arranging modules in a 2D mesh, each communicating bidirectionally with all four neighbors. Address events include a module label. Each module includes an AER router which decides how to route address events. Two routing approaches have been proposed, analyzed and tested, using either destination or source module labels. Our analyses reveal that depending on traffic conditions and network topologies either one or the other approach may result in better performance. Experimental results are given after testing the approach using high-end Virtex-6 FPGAs. The approach is proposed for both single and multiple FPGAs, in which case a special bidirectional parallel-serial AER link with flow control is exploited, using the FPGA Rocket-I/O interfaces. Extensive test results are provided exploiting convolution modules of 64x64 pixels with kernels with sizes up to 11x11, which process real sensory data from a Dynamic Vision Sensor (DVS) retina. One single Virtex-6 FPGA can hold up to 64 of these convolution modules, which is equivalent to a neural network with  262 thousand neurons and almost 32 million synapses.

Funding Research Councils:
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