A Streaming PCA VLSI Chip for Neural Data Compression.
IEEE Trans Biomed Circuits Syst
; 11(6): 1290-1302, 2017 12.
Article
em En
| MEDLINE
| ID: mdl-28809707
ABSTRACT
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. Throughout the experiments, the chip compresses LFPs by 10 at the expense of as low as 1% reconstruction errors and 144-nW/channel power consumption; for spikes, the achieved compression ratio is 25 with 8% reconstruction errors and 3.05-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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Compressão de Dados
Limite:
Humans
Idioma:
En
Revista:
IEEE Trans Biomed Circuits Syst
Ano de publicação:
2017
Tipo de documento:
Article