Hardware Acceleration for Machine Learning

Abstract

This paper presents an approach to enhance the performance of machine learning applications based on hardware acceleration. This approach is based on parameterised architectures designed for Convolutional Neural Network (CNN) and Support Vector Machine (SVM), and the associated design flow common to both. This approach is illustrated by two case studies including object detection and satellite data analysis. The potential of the proposed approach is presented.