An Event Based Gesture Recognition System Using a Liquid State Machine Accelerator

Abstract

In this paper, we design a spiking neural network (SNN) accelerator based on the Liquid State Machine (LSM) which is more lightweight and bionic. In this accelerator, 512 leaky integrate-and-fire (LIF) neurons with configurable biological parameters are integrated. For the sparsity of computation and memory of the LSM, we use zero-skipping and weight compression to maximize the performance. The quantized 4-bit model deployed on the accelerator can achieve a classification accuracy of 97.42% on the DVS128 gesture dataset. We implement the accelerator on FPGA. Results indicate that its end-to-end average inference latency is 3.97 ms, which is 26 times better than the gesture recognition system based on TrueNorth.