Enabling Machine Learning on Resource Constrained Devices by Source Code Generation of the Learned Models

Abstract

Due to the development of IoT solutions, we can observe the constantly growing number of these devices in almost every aspect of our lives. The machine learning may improve increase their intelligence and smartness. Unfortunately, the highly regarded programming libraries consume to much resources to be ported to the embedded processors. Thus, in the paper the concept of source code generation of machine learning models is presented as well as the generation algorithms for commonly used machine learning methods. The concept has been proven in the use cases.