Thesis-1998-Kane.pdf (7.66 MB)
An instruction systolic array architecture for multiple neural network types
thesis
posted on 2014-10-09, 09:27 authored by Andrew KaneModern electronic systems, especially sensor and imaging systems, are beginning to
incorporate their own neural network subsystems. In order for these neural systems to learn in
real-time they must be implemented using VLSI technology, with as much of the learning
processes incorporated on-chip as is possible. The majority of current VLSI implementations
literally implement a series of neural processing cells, which can be connected together in an
arbitrary fashion. Many do not perform the entire neural learning process on-chip, instead
relying on other external systems to carry out part of the computation requirements of the
algorithm.
The work presented here utilises two dimensional instruction systolic arrays in an attempt to
define a general neural architecture which is closer to the biological basis of neural networks - it
is the synapses themselves, rather than the neurons, that have dedicated processing units. A
unified architecture is described which can be programmed at the microcode level in order to
facilitate the processing of multiple neural network types.
An essential part of neural network processing is the neuron activation function, which can
range from a sequential algorithm to a discrete mathematical expression. The architecture
presented can easily carry out the sequential functions, and introduces a fast method of
mathematical approximation for the more complex functions. This can be evaluated on-chip,
thus implementing the entire neural process within a single system.
VHDL circuit descriptions for the chip have been generated, and the systolic processing
algorithms and associated microcode instruction set for three different neural paradigms have
been designed. A software simulator of the architecture has been written, giving results for
several common applications in the field.
History
School
- Science
Department
- Computer Science
Publisher
© Andrew KanePublisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
1998Notes
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.EThOS Persistent ID
uk.bl.ethos.286980Language
- en