Abstract
We report a neural recording system with embedded lossless compression using spatiotemporal correlation and sparsity of neural signals to reduce dynamic power (Pd) dissipation for data transmission in high-density neural recording systems. We could successfully compress the data rate of neural signals by a factor of 5.35 (local field potential, LFP) and 10.54 (action potential, AP), respectively. Consequently we reduced Pd consumption by 89% while achieving the state-of-the-art recording performance of 3.37 μW/Ch, 5.18 μVrms input-referred noise, and 3.41NEF2Vdd.
Original language | English |
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Title of host publication | 2017 Symposium on VLSI Circuits, VLSI Circuits 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | C168-C169 |
ISBN (Electronic) | 9784863486065 |
DOIs | |
Publication status | Published - 2017 Aug 10 |
Event | 31st Symposium on VLSI Circuits, VLSI Circuits 2017 - Kyoto, Japan Duration: 2017 Jun 5 → 2017 Jun 8 |
Publication series
Name | IEEE Symposium on VLSI Circuits, Digest of Technical Papers |
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Other
Other | 31st Symposium on VLSI Circuits, VLSI Circuits 2017 |
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Country/Territory | Japan |
City | Kyoto |
Period | 17/6/5 → 17/6/8 |
Bibliographical note
Publisher Copyright:© 2017 JSAP.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering