Variability analysis of Memristor-based Sigmoid Function

Abstract

Activation functions are widely used in neural networks to decide the activation value of the neural unit based upon linear combinations of the weighted inputs. The effective implementation of activation function is highly important, as they help to represent non-linear complex functional mappings between inputs and outputs of the neural network. One of the non-linear approaches is to use a sigmoid function. Therefore, there is a growing need in enhancing the performance of sigmoid circuits. In this paper, the main objective is to modify existing current mirror based sigmoid model by replacing CMOS transistors with memristor devices. This model was tested varying different circuit parameters, transistor size and temperature. The the area, power and noise in the modified CMOS-memristive sigmoid circuit are shown. The application of memristors in the sigmoid circuit results in higher component density in an on-chip area, allowing a reduction of power and area by 7$\%$. The proposed sigmoid circuit was simulated in SPICE using 180nm TSMC CMOS technology.