Mapping of Spiking Neural Network Topologies on Neuromorphic Hardware

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As we move towards edge computing, not only low power but concurrently, critical timing is demanded from the underlying hardware platform. Spiking neural networks ensure high performance and low power when run on specialized architectures like neuromorphic hardware. However, the techniques in use to configure these neural networks on massively parallel neuromorphic crossbar arrays remain sparsely explored. This motivates the research on how neural network topologies encompassing spiking architectures can be configured on a neuromorphic hardware. In this thesis, a unique placement algorithm is devised to map diverse and complex neural network architectures on a connectivity-constrained array with thousands of processing elements(PEs) within seconds. Wide spectra of SNNs with varying complexity are investigated to evaluate the feasibility of mapping on the target neuromorphic architecture involving unique connectivity constraints. The performance of the proposed ALAPIN mapper is validated through time-to-solution for the surveyed SNN schemes with varying network sizes and diverse complexity measures. Experiments show that simple networks converge within 10 milliseconds. With limited resources and as the network architectural complexity increases, hardware constraints become overwhelming to achieve placement solution within a decent time frame. Further experiments are carried out to estimate the resource utilization of each candidate SNN for varying network sizes on target hardware. Liquid state machines use a greater number of synapses for a same number of neurons than the rest of the candidates, with approximately 100% neurons, 30% input resources, and 20% synapses on target hardware.