Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157768
Title: Building highly efficient neural networks through weight pruning
Authors: Low, Xuan Hui
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Low, X. H. (2022). Building highly efficient neural networks through weight pruning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157768
Abstract: Abstract: Neural network pruning—the task of reducing the size of a neural network architecture by removing neurons/connections (or links) in the networks—has been one of the main focuses of a great deal of work in recent years. Neural network pruning reduces the size of the neural network by removing links and neurons from the neural network using a certain set of criteria. This is beneficial to help save on the cost from the creation of large corporate neural networks and at the same time without compromising too much in performance accuracy and generalisation ability of the networks. This project reports the experimental study on benchmark datasets using various neural networks
URI: https://hdl.handle.net/10356/157768
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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