Poster: Resource-Efficient Environmental Sound Classification Using Hyperdimensional Computing
Year: 2024
Type: conference-paper
Abstract: 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: Run Wang, Shirley Bian, Xiaofan Yu, Quanling Zhao, Le Zhang, Tajana Rosing
Institution: University of California San Diego
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: FET: Small: Brain-Inspired Hyperdimensional Computing for IoT Applications, AI Institute for Learning-Enabled Optimization at Scale (TILOS), CC* NPEO: Toward the National Research Platform, CCRI: ABR: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI), Category II: A Prototype National Research Platform, Collaborative Research: MLWiNS: Hyperdimensional Computing for Scalable IoT Intelligence Beyond the Edge, Collaborative Research: IIS: RI: Medium: Lifelong learning with hyper dimensional computing, CCRI: ENS: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI)
OpenAlex ID: W4404034559