ISCA: Intelligent Sense-Compute Adaptive Co-Optimization of Multimodal Machine Learning Kernels for Resilient mHealth Services on Wearables
Editor’s notes: This article proposes a sensing and computing adaptive optimization of multimodal machine learning kernels. The framework employs reinforcement learning to jointly determine sensing and compute configuration settings, which minimizes energy consumption while providing accuracy guarantees. The solution achieves up to 23% improvement in prediction accuracy compared to prior methods. —Ganapati Bhat, Washington State University, USA —Pietro Mercati, Intel Labs, USA
Authors
- Sina Shahhosseini (ORCID: https://orcid.org/0000-0002-7967-4547)
- Nikil Dutt (ORCID: https://orcid.org/0000-0002-3060-8119)
- Pasi Liljeberg (ORCID: https://orcid.org/0000-0002-9392-3589)
- Emad Kasaeyan Naeini (ORCID: https://orcid.org/0000-0002-7438-2641)
- Hamidreza Alikhani (ORCID: https://orcid.org/0000-0002-0983-1260)
- Anil Kanduri (ORCID: https://orcid.org/0000-0003-3188-8703)
- Amir M. Rahmani (ORCID: https://orcid.org/0000-0003-0725-1155)
Institutions
- University of Turku (FI)
- University of California, Irvine (US)
Publication Details
- Journal
- IEEE Design and Test
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1109/mdat.2024.3469828
- Citations
- 3
- Primary Topic
- IoT and Edge/Fog Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.05