On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various neurophysiological data. However, the state-of-the-art compressed sensing (CS) encoders leverage random but dense binary measurement matrices, which incur substantial implementation costs on both power and area that could offset the benefits from the reduced wireless data rate. In this paper, we propose two CS encoder designs based on sparse measurement matrices that could lead to efficient hardware implementation. Specifically, two different approaches for the construction of sparse measurement matrices, i.e., the deterministic quasi-cyclic array code (QCAC) matrix and $(1,s)$-sparse random binary matrix [(1,s) -SRBM] are exploited. We demonstrate that the proposed CS encoders lead to comparable recovery performance. And efficient VLSI architecture designs are proposed for QCAC-CS and (1,s)-SRBM encoders with reduced area and total power consumption.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE transactions on biomedical circuits and systems|
|State||Published - Feb 2018|
Bibliographical noteFunding Information:
Manuscript received May 21, 2017; revised July 30, 2017 and October 9, 2017; accepted November 17, 2017. Date of publication January 11, 2018; date of current version January 26, 2018. This work was supported by the startup fund provided by the University of Minnesota. The work of W. Zhao was supported by the NIH R01-MH111413. The work of T. Wu was supported by the MnDrive Neuromodulation Fellowship. This paper was recommended by Associate Editor H. Yu. (Corresponding author: Zhi Yang.) W. Zhao, T. Wu, and Z. Yang are with the Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: email@example.com; firstname.lastname@example.org; email@example.com).
This work was supported by the startup fund provided by the University of Minnesota. The work of W. Zhao was supported by the NIH R01-MH111413. The work of T. Wu was supported by the MnDrive Neuromodulation Fellowship. This paper was recommended by Associate Editor H. Yu.
- Compressed sensing
- data compression
- sparse sensing matrix
- spike sorting
- wireless neural interface