Exploiting deep learning accelerators for neuromorphic workloads
Pao-Sheng Vincent Sun,
Alexander Titterton,
Anjlee Gopiani
et al.
Abstract:Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads. 
Error backpropagation is presently regarded as the most effective method for training SNNs, but in a twist of irony, when training on modern graphics processing units (GPUs) this becomes more expensive than non-spiking networks. The emergence of Graphcore's Intelligence Processing Units (IPUs) balances the parallelized nature of… Show more
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