Analog perceptron circuit with DAC-based multiplier

This paper presents a perceptron circuit which can be implemented into a sensor analog front-end consistent with neural network-based machine learning. We introduce a DAC-based multiplier in the perceptron circuit, where the DAC is used as a programmable resistor. Compared with a traditional transconductor-based multiplier, the precision of our multiplier is formulated only by the digital codes, and it has a wide input range and a good temperature dependency. The simulation result demonstrates the DAC-based multiplier amplifies smoothly analog signal by the digital codes. Furthermore, we extend our perceptron model so as to deal with time series inputs and show a promising result by simulation. As one of an important future works, focusing on periodic signal inputs, we discuss a general architecture of perceptron circuit inspired by Fourier series.