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Efficient Deep Neural Networks Aimed for Resource Constrained Embedded Devices

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thesis
posted on 2024-04-03, 06:05 authored by LUIS ENRIQUE GUERRA FERNANDEZ
The problem of excessive computational resource requirements in deep neural networks is attacked from different angles, aiming at optimizing three resources without sacrificing accuracy: latency, storage and energy consumption. This will enable mobile phones and highly resource constrained devices such as specific purpose embedded devices, to run deep networks on the edge rather than on the cloud.

History

Campus location

Australia

Principal supervisor

Tom Drummond

Additional supervisor 1

Professor Lindsay Kleeman

Year of Award

2024

Department, School or Centre

Electrical and Computer Systems Engineering

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Engineering

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