Modeling and Simulation of Asynchrony in Neuromorphic Computing

Abstract

Neuromorphic computing is a non-von Neumann architecture which is also referred to as artificial neural network and that allows electronic system to function in the same manner as that of the human brain. In this paper we have developed neural core architecture analogous to that of the human brain. Each neural core has its own computational element neuron, memory to store information and local clock generator for synchronous functioning of neuron along with asynchronous input-output port and its port controller. The neuron model used here is a tailor-made of IBM TrueNorth’s neuron block. Our design methodology includes both synchronous and asynchronous circuit in order to build an event-driven neural network core. We have first simulated our design using Neuroph studio in order to calculate the weights and bias value and then used these weights for hardware implementation. With that we have successfully demonstrated the working of neural core using XOR application. It was designed in VHDL language and simulated in Xilinx ISE software.