Sponsor
Portland State University. Department of Electrical and Computer Engineering
First Advisor
Marek Perkowski
Term of Graduation
Spring 2022
Date of Publication
6-2-2022
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.) in Electrical and Computer Engineering
Department
Electrical and Computer Engineering
Language
English
Subjects
Decomposition (Mathematics), Partitions (Mathematics), Machine learning
DOI
10.15760/etd.7917
Physical Description
1 online resource (xix, 372 pages)
Abstract
This dissertation presents a novel design of a hardware classifier based on combining modified Ashenhurst-Curtis Decomposition and multiplexer-based synthesis. The PSUD classifier brings three new contributions: an approach to solve the column multiplicity problem, an approach to encode multiple-valued variables, and a decomposition algorithm based on modified Ashenhurst-Curtis Decomposition. One of the biggest challenges in Boolean function decomposition is the variable partitioning problem. Thus, we introduce a new representation of two combined classifiers for multiple-valued functions to overcome the variable partitioning problem which allows finding the minimal column multiplicity and consequently to find high quality decompositions leading to a good learning accuracy. Another aspect of our approach is that the trained classifier is a Boolean network realized in an FPGA which allows for fast object recognition by a robot. The classifier gives very good accuracy results when tested on multi-valued Machine Learning benchmarks from the UC Irvine repository.
Rights
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Persistent Identifier
https://archives.pdx.edu/ds/psu/38060
Recommended Citation
Al-Askaar, Saad Mohammad, "A New Approach to Machine Learning Hardware Classifier Design Based on Functional Decomposition of Multi-Valued Functions" (2022). Dissertations and Theses. Paper 6047.
https://doi.org/10.15760/etd.7917