Open Collections

Faltyn, Mateusz

University of British Columbia

Computer vision models, such as image and video classifiers, are increasingly prevalent in Internet-of-Things systems. Since the advent of the AlexNet neural network model in 2012, convolutional neural networks have been demonstrated to be very effective at performing many computer vision tasks. However, convolutional neural networks’ high computational and storage costs hinder the wider adoption of computer vision models in smaller Internet-of-Things devices such as mobile phones or embedded systems. As larger neural network models increase hardware costs, industry and academia have come together to tackle the problem of how to compress convolutional neural networks. Convolutional neural network compression via tensor decomposition has been shown to reduce the memory and storage requirements for devices to perform computer vision tasks successfully. In this work, we first review the preliminaries of tensor decomposition and define the four major types of tensor decompositions and their related decomposition algorithms in Chapter 1. Afterward, we introduce the building blocks of neural networks and describe convolutional neural networks in Chapter 2. Finally, we overview the different tensor decomposition approaches for convolutional neural network compression and display the results of two experiments using the PyTorch-TedNet CIFAR10 and CIFAR100 model benchmarks in Chapter 3.

Text

2023-02-02

Attribution 4.0 International

http://hdl.handle.net/2429/83778

Mathematics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by/4.0/