A Cross-layer based mapping for spiking neural network onto network on chip

Abstract

Network-on-Chip provides a packet-based and scalable inter-connected structure for spiking neural networks. However, existing neural mapping methods just distribute all neurons of a population into an on-chip network core or nearby cores sequentially. As there is no connection among population, the population based mapping degrades inter-neuron communicating performance between different cores. This paper presents a Cross-LAyer based neural MaPping method that maps synaptic connected neurons belonging to adjacent layers into the same on-chip network node. In order to adapt to various input patterns, the strategy also takes input spike rate into consideration and remap neurons for improving mapping efficiency. The method helps to reduce inter-core communication cost. The experimental results demonstrate the efficient results of the proposed mapping strategy in the aspect of spike transfer latency as well as dynamic energy cost improvement. In the applications of handwritten digits and edge extraction, in which the type of interconnection among neurons is different, the neural mapping algorithm reduces spike average transfer latency by maximum 42.83%, and reduces dynamic energy by maximum 36.29%.