Advancing interconnect density for spiking neural network hardware implementations using traffic-aware adaptive network-on-chip routers

Neural Networks : the Official Journal of the International Neural Network Society
Snaider CarrilloFearghal Morgan

Abstract

The brain is highly efficient in how it processes information and tolerates faults. Arguably, the basic processing units are neurons and synapses that are interconnected in a complex pattern. Computer scientists and engineers aim to harness this efficiency and build artificial neural systems that can emulate the key information processing principles of the brain. However, existing approaches cannot provide the dense interconnect for the billions of neurons and synapses that are required. Recently a reconfigurable and biologically inspired paradigm based on network-on-chip (NoC) and spiking neural networks (SNNs) has been proposed as a new method of realising an efficient, robust computing platform. However, the use of the NoC as an interconnection fabric for large-scale SNNs demands a good trade-off between scalability, throughput, neuron/synapse ratio and power consumption. This paper presents a novel traffic-aware, adaptive NoC router, which forms part of a proposed embedded mixed-signal SNN architecture called EMBRACE (EMulating Biologically-inspiRed ArChitectures in hardwarE). The proposed adaptive NoC router provides the inter-neuron connectivity for EMBRACE, maintaining router communication and avoiding dropped router pac...Continue Reading

References

Apr 26, 2002·Neural Computation·Werner M Kistler, Wulfram Gerstner
Oct 16, 2004·IEEE Transactions on Neural Networks·Eugene M Izhikevich
Jan 24, 2006·Nature Reviews. Neuroscience·Henry Markram
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Sep 30, 2010·IEEE Transactions on Neural Networks·John J WadeHeather M Sayers

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Citations

Oct 28, 2014·Communicative & Integrative Biology·Ben de Lacy Costello, Andrew I Adamatzky
Mar 14, 2017·IEEE Transactions on Neural Networks and Learning Systems·Junxiu LiuJohn J Wade
Dec 14, 2018·Frontiers in Neuroscience·Yuling LuoXuemei Ding

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