Date of Award

2014

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Electrical and Computer Engineering

First Advisor

Mirhassani, Mitra

Keywords

Applied sciences, Continuous valued number system, Mixed-signal, Neural networks, Vlsi implementation

Rights

CC BY-NC-ND 4.0

Abstract

In this work, mixed-signal implementation of Continuous Valued Number System (CVNS) neural network is proposed. The proposed network resolves the limited signal processing precision issue present in mixed-signal neural networks. This is realized by the CVNS addition, the CVNS multiplication and the CVNS sigmoid function evaluation algorithms proposed in this dissertation. The proposed algorithms provide accurate results in low-resolution environment. In addition, an area-efficient low sensitivity CVNS Madaline is proposed. The proposed Madaline is more robust to input and weight errors when compared to the previously developed structures. Moreover, its area consumption is lower. Furthermore, a new approximation scheme for hyperbolic tangent activation function is proposed. Using the proposed approximation scheme results in efficient implementation of digital ASIC neural networks in terms of area, delay and power consumption.

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