MSFANC: A Case Study of Memory-Based Computing for Wearable Device Applications
This letter emphasizes memory-based computing’s potential in enhancing efficiency for wearable devices with limited resources, focusing on active noise control (ANC) in wireless earbuds. Our proposed memory-based selective fixed-filter ANC (MSFANC) scheme reduces power consumption and delay through lookup table (LUT) computation and simplified filter matching. Theoretical analysis and simulations demonstrate MSFANC’s effectiveness, offering a new paradigm for resource-constrained wearable systems with non-volatile memories’ (NVM) support.
Authors
- Weibang Dai
- Shunfen Li (ORCID: https://orcid.org/0000-0002-2464-1414)
- Yichen Song (ORCID: https://orcid.org/0009-0005-8716-8088)
- Houpeng Chen (ORCID: https://orcid.org/0000-0002-5766-5746)
- Sannian Song (ORCID: https://orcid.org/0000-0001-7186-4744)
- Tao Hong (ORCID: https://orcid.org/0000-0003-2421-7290)
- Zhitang Song (ORCID: https://orcid.org/0000-0001-7859-9429)
- Xiaogang Chen (ORCID: https://orcid.org/0000-0003-1478-040X)
Institutions
- Shanghai Institute of Microsystem and Information Technology (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1587/transfun.2024eal2074
- Citations
- 1
- Primary Topic
- Cloud Computing and Resource Management
- Type
- article
- Field-Weighted Citation Impact
- 0.78