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Multi-casting mesh AER: A scalable assembly approach for reconfigurable neuromorphic structured AER systems. Application to ConvNets

AuthorsZamarreño-Ramos, Carlos ; Linares-Barranco, Alejandro; Serrano-Gotarredona, Teresa ; Linares-Barranco, Bernabé
Issue Date2013
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Transactions on Biomedical Circuits and Systems 7(1): 82-102 (2013)
AbstractThis 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 64 × 64 pixels with kernels with sizes up to 11 × 11, 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 × 103 neurons and almost 32 million synapses.
DescriptionEl pdf del artículo es la versión post-print.
Publisher version (URL)http://dx.doi.org/10.1109/TBCAS.2012.2195725
Appears in Collections:(IMSE-CNM) Artículos
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