Exploring the Random Network of Hodgkin and Huxley Neurons with Exponential Synaptic Conductances on OpenCL FPGA Platform

Abstract

We choose a random network of Hodgkin–Huxley (HH) neurons with exponential synaptic conductance as a study of accelerating the simulation of networks of spiking neurons on an FPGA. Focused on the conductance-based HH (COBAHH) benchmark, we execute the benchmark on a general-purpose simulator for spiking neural networks, identify a computationally intensive kernel in the generated C++ code, convert the kernel to a portable OpenCL kernel, and describe the kernel optimizations which can reduce the resource utilizations and improve the kernel performance. We evaluate the kernel on an Intel Arria 10 based FPGA platform, an Intel Xeon 16-core CPU, and an NVIDIA Tesla P100 GPU. FPGAs are promising for the simulation of spiking neuron network.