A streaming PCA based VLSI chip for neural data compression

Tong Wu; Wenfeng Zhao; Hongsun Guo; Hubert Lim; Zhi Yang

doi:10.1109/BioCAS.2016.7833764

A streaming PCA based VLSI chip for neural data compression

Tong Wu, Wenfeng Zhao, Hongsun Guo, Hubert Lim, Zhi Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

Local field potentials (LFPs) are popularly used in wireless neural interface due to its chronic stability and robustness against noise and radio interferences. On-chip data compression is advantageous that allows for integration with the recent low-power, low-data-rate wireless technologies to ensure reliable operations. In this paper, we propose a streaming principal component analysis (PCA) based algorithm and its microchip implementation to compress multichannel LFP data. The chip has been designed in a 65nm CMOS technology and occupies a silicon area of 0.06mm². It has been tested with Guinea pig auditory cortex data recorded with a multi-shank Neuro Nexus probe, where the chip can achieve an 8× compression ratio with ∼3% average reconstruction error, consuming 144nW per channel at a 0.5V power supply.

Original language	English (US)
Title of host publication	Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	192-195
Number of pages	4
ISBN (Electronic)	9781509029594
DOIs	https://doi.org/10.1109/BioCAS.2016.7833764
State	Published - 2016
Event	12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016 - Shanghai, China Duration: Oct 17 2016 → Oct 19 2016

Publication series

Name	Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016

Other

Other	12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
Country/Territory	China
City	Shanghai
Period	10/17/16 → 10/19/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/BioCAS.2016.7833764

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Wu, T., Zhao, W., Guo, H., Lim, H., & Yang, Z. (2016). A streaming PCA based VLSI chip for neural data compression. In Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016 (pp. 192-195). Article 7833764 (Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BioCAS.2016.7833764

A streaming PCA based VLSI chip for neural data compression. / Wu, Tong; Zhao, Wenfeng; Guo, Hongsun et al.
Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 192-195 7833764 (Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wu, T, Zhao, W, Guo, H, Lim, H & Yang, Z 2016, A streaming PCA based VLSI chip for neural data compression. in Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016., 7833764, Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016, Institute of Electrical and Electronics Engineers Inc., pp. 192-195, 12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016, Shanghai, China, 10/17/16. https://doi.org/10.1109/BioCAS.2016.7833764

Wu T, Zhao W, Guo H, Lim H , Yang Z. A streaming PCA based VLSI chip for neural data compression. In Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 192-195. 7833764. (Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016). doi: 10.1109/BioCAS.2016.7833764

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abstract = "Local field potentials (LFPs) are popularly used in wireless neural interface due to its chronic stability and robustness against noise and radio interferences. On-chip data compression is advantageous that allows for integration with the recent low-power, low-data-rate wireless technologies to ensure reliable operations. In this paper, we propose a streaming principal component analysis (PCA) based algorithm and its microchip implementation to compress multichannel LFP data. The chip has been designed in a 65nm CMOS technology and occupies a silicon area of 0.06mm2. It has been tested with Guinea pig auditory cortex data recorded with a multi-shank Neuro Nexus probe, where the chip can achieve an 8× compression ratio with ∼3% average reconstruction error, consuming 144nW per channel at a 0.5V power supply.",

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A streaming PCA based VLSI chip for neural data compression

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