Title

A Mixed-Signal Feed-Forward Neural Network Architecture Using A High-Resolution Multiplying D/A Conversion Method

Date of Award

2013

Degree Type

Thesis

Degree Name

M.A.Sc.

Department

Electrical and Computer Engineering

First Advisor

Mitra Mirhassani

Second Advisor

Majid Ahmadi

Keywords

Electrical engineering

Rights

CC BY-NC-ND 4.0

Abstract

Artificial Neural Networks (ANNs) are parallel processors capable of learning from a set of sample data using a specific learning rule. Such systems are commonly used in applications where human brain may surpass conventional computers such as image processing, speech/character recognition, intelligent control and robotics to name a few. In this thesis, a mixed-signal neural network architecture is proposed employs a high resolution Multiplying Digital to Analog Converter (MDAC) designed using Delta Sigma Modulation (DSM). To reduce chip are, multiplexing is used in addition to analog implementation of arithmetic operations. This work employs a new method for filtering the high bit-rate signals using neurons nonlinear transfer function already existing in the network. Therefore, a configuration of a few MOS transistors are replacing the large resistors required to implement the low-pass filter in the network. This configuration noticeably decreases the chip area and also makes multiplexing feasible for hardware implementation.