Explore a cutting-edge approach to energy management in wearable IoT devices through this 21-minute conference talk from the tinyML Research Symposium 2022. Delve into the concept of tinyMAN, a lightweight energy manager that leverages reinforcement learning for energy harvesting in wearable IoT devices. Follow PhD student Toygun BASAKLAR from the University of Wisconsin - Madison as he presents the framework's deployment, data collection process, and the reinforcement learning environment. Gain insights into the results, deployability, and potential impact of this innovative solution for achieving energy-neutral operation in wearable technology. Conclude with an overview of the project's sponsors and its implications for the future of energy-efficient IoT devices.
tinyMAN - Lightweight Energy Manager Using Reinforcement Learning for Energy Harvesting Wearable IoT Devices
Overview
Syllabus
Introduction
Energy Neutral Operation
Framework Deployment
Data Collection
Environment
RL Framework
Results
Deployability
Conclusion
Sponsors
Taught by
tinyML
