A Streaming PCA VLSI Chip for Neural Data Compression

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

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Neural recording system miniaturization and integration with low-power wireless technologies require compressing neural data before transmission. Feature extraction is a procedure to represent data in a low-dimensional space; its integration into a recording chip can be an efficient approach to compress neural data. In this paper, we propose a streaming principal component analysis algorithm and its microchip implementation to compress multichannel local field potential (LFP) and spike data. The circuits have been designed in a 65-nm CMOS technology and occupy a silicon area of 0.06 mm$^2$. Throughout the experiments, the chip compresses LFPs by 10$\times$ at the expense of as low as 1% reconstruction errors and 144-nW/channel power consumption; for spikes, the achieved compression ratio is 25$\times$ with $\sim$8% reconstruction errors and 3.05-$\mu$W/channel power consumption. In addition, the algorithm and its hardware architecture can swiftly adapt to nonstationary spiking activities, which enables efficient hardware sharing among multiple channels to support a high-channel count recorder.

Original languageEnglish (US)
Article number8010351
Pages (from-to)1290-1302
Number of pages13
JournalIEEE transactions on biomedical circuits and systems
Issue number6
StatePublished - Dec 2017

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  • Feature extraction
  • neural signal processing
  • on-chip data compression
  • streaming PCA


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