Machine Learning Using Hardware and Software

For developers, advances in hardware and software for machine learning (ML) promise to bring these sophisticated methods to Internet of Things (IoT) edge devices. As this field of research evolves, however, developers can easily find themselves immersed in the deep theory behind these techniques instead of focusing on currently available solutions to help them get an ML-based design to market. To help designers get moving more quickly, this article briefly reviews the objectives and capabilities of ML, the ML development cycle, and the architecture of a basic fully connected neural network and a convolutional neural network (CNN). It then discusses the frameworks, libraries, and drivers that are enabling mainstream ML applications. It concludes by showing how general purpose processors and FPGAs can serve as the hardware platform for implementing machine learning algorithms. A subset of artificial intelligence (AI), ML encompasses a wide range of methods and algorithms.