Portland State University. Department of Electrical and Computer Engineering
Term of Graduation
Date of Publication
Doctor of Philosophy (Ph.D.) in Electrical and Computer Engineering
Electrical and Computer Engineering
1 online resource (xix, 372 pages)
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.
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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.
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