000 02491 am a22003373u 4500
042 _adc
100 1 0 _aHe, Yuming
_eauthor
_92761
700 1 0 _aCorradi, Federico
_eauthor
_92762
700 1 0 _aShi, Chengyao
_eauthor
_92763
700 1 0 _avan der Ven, Stan
_eauthor
_92764
700 1 0 _aTimmermans, Martijn
_eauthor
_92765
700 1 0 _aStuijt, Jan
_eauthor
_92766
700 1 0 _aDetterer, Paul
_eauthor
_92767
700 1 0 _aHarpe, Pieter
_eauthor
_92768
700 1 0 _aLindeboom, Lucas
_eauthor
_92769
700 1 0 _aHermeling, Evelien
_eauthor
_92770
700 1 0 _aLangereis, Geert
_eauthor
_92771
700 1 0 _aChicca, Elisabetta
_eauthor
_92772
700 1 0 _aLiu, Yao-Hong
_eauthor
_92773
245 0 0 _aAn Implantable Neuromorphic Sensing System Featuring Near-sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves
260 _c2022-10.
500 _a/pmc/articles/PMC7614138/
500 _a/pubmed/36741239
520 _aThis paper presents a bio-inspired event-driven neuromorphic sensing system (NSS) capable of performing on-chip feature extraction and "send-on-delta" pulse-based transmission, targeting peripheral-nerve neural recording applications. The proposed NSS employs event-based sampling which, by leveraging the sparse nature of electroneurogram (ENG) signals, achieves a data compression ratio of >125×, while maintaining a low normalized RMS error of 4% after reconstruction. The proposed NSS consists of three sub-circuits. A clockless level-crossing (LC) ADC with background offset calibration has been employed to reduce the data rate, while maintaining a high signal to quantization noise ratio. A fully synthesized spiking neural network (SNN) extracts temporal features of compound action potential signals consumes only 13 μW. An event-driven pulse-based body channel communication (Pulse-BCC) with serialized address-event representation encoding (AER) schemes minimizes transmission energy and form factor. The prototype is fabricated in 40-nm CMOS occupying a 0.32-mm(2) active area and consumes in total 28.2 μW and 50 μW power in feature extraction and full diagnosis mode, respectively. The presented NSS also extracts temporal features of compound action potential signals with 10-μs precision.
540 _a
546 _aen
690 _aArticle
655 7 _aText
_2local
786 0 _nIEEE J Solid-State Circuits
856 4 1 _uhttp://dx.doi.org/10.1109/JSSC.2022.3193846
_zConnect to this object online.
999 _c464
_d464