  Neuromorphic hardware platforms implement biological neurons and synapses to
execute spiking neural networks (SNNs) in an energy-efficient manner. We
present SpiNeMap, a design methodology to map SNNs to crossbar-based
neuromorphic hardware, minimizing spike latency and energy consumption.
SpiNeMap operates in two steps: SpiNeCluster and SpiNePlacer. SpiNeCluster is a
heuristic-based clustering technique to partition SNNs into clusters of
synapses, where intracluster local synapses are mapped within crossbars of the
hardware and inter-cluster global synapses are mapped to the shared
interconnect. SpiNeCluster minimizes the number of spikes on global synapses,
which reduces spike congestion on the shared interconnect, improving
application performance. SpiNePlacer then finds the best placement of local and
global synapses on the hardware using a meta-heuristic-based approach to
minimize energy consumption and spike latency. We evaluate SpiNeMap using
synthetic and realistic SNNs on the DynapSE neuromorphic hardware. We show that
SpiNeMap reduces average energy consumption by 45% and average spike latency by
21%, compared to state-of-the-art techniques.
