A Bandwidth-Efficient Emulator of Biologically-Relevant Spiking Neural Networks on FPGA

Abstract

Closed-loop experiments involving biological and artificial neural networks would improve the understanding of neural cells functioning principles and lead to the development of new generation neuroprosthesis. Several technological challenges require to be faced, as the development of real-time spiking neural network emulators which could bear the increasing amount of data provided by new generation High-Density Multielectrode Arrays. This work focuses on the development of a real-time spiking neural network emulator addressing fully-connected neural networks. This work presents a new way to increase the number of synapses supported by real-time neural network accelerators. The proposed solution has been implemented on the Xilinx Zynq 7020 All-Programmable SoC and can emulate fully connected spiking neural networks counting up to 3,098 Izhikevich neurons and 9.6e6 synapses in real-time, with a resolution of 0.1 ms.