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Title: Classification Algorithms for Embedded Hardware
Authors: Wharton, David
Advisors: Verma, Naveen
Department: Electrical Engineering
Class Year: 2014
Abstract: There are many trade-offs that arise when designing applications for embedded hardware, specifically algorithms for classification, operating on low-power embedded hardware with machine learning accelerators that necessitate good engineering decisions to be made. The trade-offs explored by this work are classification accuracy, energy consumption and mem- ory requirements. Three applications in the computer vision domain are the focus of this work.
Extent: 68 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Electrical Engineering, 1932-2016

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