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.
|Title of host publication||2017 Symposium on VLSI Circuits, VLSI Circuits 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2017 Aug 10|
|Event||31st Symposium on VLSI Circuits, VLSI Circuits 2017 - Kyoto, Japan|
Duration: 2017 Jun 5 → 2017 Jun 8
|Name||IEEE Symposium on VLSI Circuits, Digest of Technical Papers|
|Other||31st Symposium on VLSI Circuits, VLSI Circuits 2017|
|Period||17/6/5 → 17/6/8|
Bibliographical notePublisher Copyright:
© 2017 JSAP.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering