Project Motivation and Goals

The k-Nearest-Neighbours (kNN) algorithm is a popular Machine Learning technique that can be used for a variety of supervised classification tasks. In contrast to other machine learning algorithms which "encode" the knowledge gained from training data to a set of parameters, such as weights and biases, the parameter set of a kNN classifier consists of just labelled training examples. Classification of an unlabelled example takes place by calculating its Euclidean distance (or any other type of distance metric) from all the stored training examples.

A small SoC development framework to support research demonstrator designs

Example case-study of using the Arm CMSDK AMBA-AHB Bus-Matrix tools to build system-optimised interconnect.

This 4 month PhD Interdisciplinary Team Project used an Arm M7 to measure actual energy used in different forms of inference and feature extraction for keyword spotting.