MSFANC: A Case Study of Memory-Based Computing for Wearable Device Applications

Weibang DAI; Xiaogang CHEN; Houpeng CHEN; Sannian SONG; Yichen SONG; Shunfen LI; Tao HONG; Zhitang SONG

doi:10.1587/transfun.2024EAL2074

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MSFANC: A Case Study of Memory-Based Computing for Wearable Device Applications

Weibang DAI, Xiaogang CHEN, Houpeng CHEN, Sannian SONG, Yichen SONG, Shunfen LI, Tao HONG, Zhitang SONG

著者情報

キーワード: memory-based computing, lookup table, wearable devices, active noise control, emerging NVM

ジャーナルフリー早期公開

論文ID: 2024EAL2074

DOI https://doi.org/10.1587/transfun.2024EAL2074

この記事には本公開記事があります。

The final version of this article is now available: Vol. E108.A (2025), No. 6 pp. 851-855

詳細

抄録

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.

責任著者(Corresponding author)

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