Hodgkin-Huxley-Based Neural Simulation with Networks Connecting to Near-Neighbor Neurons

Abstract

Neural simulation is a very useful methodology for improving our understanding of the functions of brains, and expected to be applied to a number of practical applications related to autonomous systems and machines. However, computation time required for biophysically-meaningful simulation is often the limiting factor to mimic the large-scale neural network even if we implement custom hardware simulators on FPGAs. To overcome this issue, we focus on the spatial network connectivity of biological neurons and attempt to design a truly dataflow pipeline on FPGAs aiming at real-time Hodgkin-Huxley based simulation against more than 10,000 neurons. We implement our design on Xilinx Alveo U200 with HLS toolchains provided by Maxeler. From the results of evaluation, we find that mapping locality of neurons to the network connecting only to neighboring ones dramatically contributes to reducing the overheads for accumulation at the gap junction calculation. We demonstrate that our accelerator design can perform real-time simulation of network consisting 20736 neurons, which is 66.8 times larger than the existing implementation.