A Hardware Implementation of SNN-Based Spatio-Temporal Memory Model

Front Neurosci. 2019 Aug 9:13:835. doi: 10.3389/fnins.2019.00835. eCollection 2019.

Abstract

Simulating human brain with hardware has been an attractive project for many years, since memory is one of the fundamental functions of our brains. Several memory models have been proposed up to now in order to unveil how the memory is organized in the brain. In this paper, we adopt spatio-temporal memory (STM) model, in which both associative memory and episodic memory are analyzed and emulated, as the reference of our hardware network architecture. Furthermore, some reasonable adaptations are carried out for the hardware implementation. We finally implement this memory model on FPGA, and additional experiments are performed to fine tune the parameters of our network deployed on FPGA.

Keywords: FPGA; brain-inspired; memory model; neuromorphic hardware; spatio-temporal memory; spike neural network.