Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware

11 Jun 2020Adarsha BalajiThibaut MartyAnup DasFrancky Catthoor

In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at {run-time}. Our design methodology operates in two steps -- step 1 is a layer-wise greedy approach to partition SNNs into clusters of neurons and synapses incorporating the constraints of the neuromorphic architecture, and step 2 is a hill-climbing optimization algorithm that minimizes the total spikes communicated between clusters, improving energy consumption on the shared interconnect of the architecture... (read more)

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