DOI: https://doi.org/10.15368/theses.2020.76
Available at: https://digitalcommons.calpoly.edu/theses/2136
Date of Award
4-2020
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
College
College of Engineering
Advisor
Xia-Hua Yu
Advisor Department
Electrical Engineering
Advisor College
College of Engineering
Abstract
Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made in effectively reducing these overheads by pruning and compressing large CNNs with only a slight decline in model accuracy. In this study, two pruning methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase interleaved with a finetuning phase. It is shown that when performing the scale-factor pruning algorithm on ECG, finetuning time can be expedited by 1.4 times over the traditional approach with only 10% of expensive floating-point operations retained, while experiencing no significant impact on accuracy.
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Artificial Intelligence and Robotics Commons, Biomedical Commons, Data Science Commons, Software Engineering Commons, Theory and Algorithms Commons