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|Title:||Classification Algorithms for Embedded Hardware|
|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.|
|Type of Material:||Princeton University Senior Theses|
|Appears in Collections:||Electrical Engineering, 1932-2017|
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