Machine Learning on Microcontrollers

Abstract

Machine Learning has made remarkable progress in the past decade, but current models are computationally expensive. This thesis focuses on deploying machine learning models on microcontrollers. MobileNet based model was trained for two projects: A person detection model was trained, achieving 78.6% accuracy with 225 KB size and 170 KB RAM usage on an ESP32-CAM board. The board sends detected images to a webserver for further analysis. In an another project, keyword detection model was trained, achieving 91.12% accuracy with 120 KB size and 38 KB RAM usage, deployed on a custom made ESP32-based PCB with a MEMS microphone.