VLSI Implementation of Neural Systems

Abstract

A unique strategy for optimum multi-objective optimization for VLSI implementation of artificial neural network (ANN) is proposed. This strategy is efficient in terms of area, power, and speed, and it has a good degree of accuracy and dynamic range. The goal of this research is to find the sweet spot where area, speed, and power may all be optimised in a very large-scale integration (VLSI) implementation of a neural network (NN). The design should also allow for the dynamic reconfiguration of weight, and it should be very precise. The authors also use a 65-nm CMOS fabrication method to produce the circuits, and these results show that the suggested integral stochastic design may reduce energy consumption by up to 21% compared to the binary radix implementation, without sacrificing accuracy.