TinyML Techniques for running Machine Learning models on Edge Devices

Abstract

Resource-constrained platforms such as micro-controllers are the workhorses in embedded systems, being deployed to capture data from sensors and send the collected data to cloud for processing. Recently, a great interest is seen in the research community and industry to use these devices for performing Artificial Intelligence/Machine Learning (AI/ML) inference tasks in the areas of computer vision, natural language processing, machine monitoring etc. leading to the realization of embedded intelligence at the edge. This task is challenging and needs a significant knowledge of AI/ML applications, algorithms, and computer architecture and their interactions to achieve the desired performance. In this tutorial we cover a few aspects that will help embedded systems designers and AI/ML engineers and scientists to deploy the AI/ML models on the Tiny Edge Devices at an optimum level of performance.