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.