Poster: Resource-Efficient Environmental Sound Classification Using Hyperdimensional Computing
On-device environmental sound classification (ESC) in rural areas faces one major challenge of resource efficiency. Traditional methods rely on resource-intensive machine learning models, making them impractical for small edge devices like microcontrollers (MCUs). This poster presents SoundHD, a novel ESC solution using Hyperdimensional Computing (HDC), a brain-inspired and lightweight computing paradigm. We further optimize the memory footprint for deployment on MCUs. Our initial results show that SoundHD can be deployed and executed effectively on memory-constrained MCUs.
Authors
- Xiaofan Yu (ORCID: https://orcid.org/0000-0002-9638-6184)
- Tajana Rosing (ORCID: https://orcid.org/0000-0002-6954-997X)
- Quanling Zhao (ORCID: https://orcid.org/0000-0003-4699-5149)
- R Wang
- Le Zhang (ORCID: https://orcid.org/0009-0002-2699-8588)
- Shirley Bian
Institutions
- University of California San Diego (US)
Publication Details
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1145/3666025.3699427
- Primary Topic
- Ferroelectric and Negative Capacitance Devices
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Defense Advanced Research Projects Agency