Demo: Battery-free TinyML Made Easy with Riotee
This demo uses a machine-learning-based hot word detection application to showcase the capabilities of Riotee, an open-source and commercially available hardware-software platform for the battery-free Internet of Things. We describe the Riotee hardware consisting of a base module, a debug probe for easy firmware updates, and several expansion boards that enhance functionality without the need for custom-designed printed circuit boards (PCBs). The demo features the classification of live audio recordings using TinyML deep neural network inference aboard a Riotee device. The Riotee device transmits the classification results via Bluetooth Low Energy (BLE) to a smartphone given to visitors. Visitors can also observe how the Riotee software checkpoints and restores critical state in case of power failures via the visualization of logic analyzer traces.
Authors
- Marco Zimmerling (ORCID: https://orcid.org/0000-0003-1450-2506)
- Kai Geissdoerfer (ORCID: https://orcid.org/0000-0002-4899-7466)
Institutions
- Technische Universität Darmstadt (DE)
Publication Details
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1145/3666025.3699419
- Primary Topic
- Parallel Computing and Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft