A memory-efficient routing method for large-scale spiking neural networks

Progress in VLSI technologies is enabling the integration of large numbers of spiking neural network processing modules into compact systems. Asynchronous routing circuits are typically employed to efficiently interface these modules, and configurable memory is usually used to implement synaptic connectivity among them. However, supporting arbitrary network connectivity with conventional routing methods would require prohibitively large memory resources. We propose a two stage routing scheme which minimizes the memory requirements needed to implement scalable and reconfigurable spiking neural networks with bounded connectivity. Our routing methodology trades off network configuration flexibility for routing memory demands and is optimized for the most common and anatomically realistic neural network topologies. We describe and analyze our routing method and present a case study with a large neural network.