Fast simulation of Digital Spiking Silicon Neuron model employing reconfigurable dataflow computing

Abstract

A new simulation scheme of the Digital Spiking Silicon Neuron (DSSN) model is proposed. This scheme is based on the reconfigurable dataflow computing paradigm and targets the Maxeler MaxWorkstation. Compared to the previous implementation of the DSSN network, the new scheme has the virtues of better flexibility and better programmability. More importantly, computing with dataflow cores takes good advantage of the intrinsic parallelism of the reconfigurable hardware and better pipelining is achievable. The proposed scheme has good potential of conducting large-scale and fast simulation of the DSSN-model-based network which is pivotal to future neuroscience research.