Perceptron Linear Function Design with CMOS-Memristive Circuits

TL;DR

This paper presents a CMOS-memristive circuit design for perceptrons with linear activation functions, analyzing its performance under temperature variations and noise, advancing analog neural network implementations.

Contribution

It introduces a novel perceptron circuit with linear activation using operational amplifiers and memristive crossbars, addressing open challenges in analog activation function design.

Findings

Performance varies with temperature and noise levels.

The circuit demonstrates stable linear activation behavior.

Potential for high-speed analog neural network applications.

Abstract

In the last decade, the interest to emulation of the functionality and structure of the human brain to solve the problems related to image processing and pattern recognition, especially using to Artificial Neural Network (ANN), has increased. Since the capability of ANN to compute at high-speed has been proven to be very useful for various computational problems. One of the simple ANN models is perceptron. Since the perceptron is the basic form of a neural network, the efficient implementation of analog activation functions is required. As various works introduce the design of sigmoid and tangent activation functions, the other activation functions remain an open research problem. This paper describes the design of the perception circuit with the linear activation function using operational amplifier and memristive crossbar. Additionally, the variation of performance with temperature, noise of the circuit is presented.