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  5. Configurable Low Power Analog Multilayer Perceptron
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Configurable Low Power Analog Multilayer Perceptron

Date Issued
May 12, 2018
Author(s)
Dix, Jeffery M.  
Advisor(s)
Benjamin J. Blalock
Additional Advisor(s)
Vasilios Alexiades
Jeremy H. Holleman III
Lynne E. Parker
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/26231
Abstract

A configurable, low power analog implementation of a multilayer perceptron (MLP) is presented in this work. It features a highly programmable system that allows the user to create a MLP neural network design of their choosing. In addition to the configurability, this neural network provides the ability of low power operation via analog circuitry in its neurons. The main MLP system is made up of 12 neurons that can be configurable to any number of layers and neurons per layer until all available resources are utilized. The MLP network is fabricated in a standard 0.13 μm CMOS process occupying approximately 1 mm2 of on-chip area. The MLP system is analyzed at several different configurations with all achieving a greater than 1 Tera-operations per second per Watt figure of merit. This work offers a high speed, low power, and scalable alternative to digital configurable neural networks.

Subjects

analog

low power

multilayer perceptron...

configurable

neural networks

Degree
Doctor of Philosophy
Major
Electrical Engineering
File(s)
Thumbnail Image
Name

utk.ir.td_1119.pdf

Size

10.91 MB

Format

Adobe PDF

Checksum (MD5)

1d4e8771a8029be9c4b268570fc6cabf

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