Hardware event-driven simulation engine for spiking neural networks

Abstract

The efficient simulation of spiking neural networks (SNN) remains an open challenge. Current SNN computing engines are still far away from simulating systems of millions of neurons efficiently. This contribution describes a computing scheme that takes full advantage of the massive parallel processing resources available at FPGA devices. The computing engine adopts an event-driven simulation scheme and an efficient next-event-to-go searching method to achieve high performance. We have designed a pipelined datapath, in order to compute several events in parallel avoiding idle computing resources. The system is able to compute approximately 2.5 million spikes per second. The whole computing machine is composed only of an FPGA device and five external memory SRAM chips. Therefore, the presented approach is of high interest for simulation experiments that require embedded simulation engines (for instance, in robotic experiments with autonomous agents).