@inproceedings{Leene:2017:10.1109/BioCAS.2016.7833806,
author = {Leene, L and Constandinou, TG},
doi = {10.1109/BioCAS.2016.7833806},
pages = {360--363},
publisher = {IEEE},
title = {A 2.7uW/Mips, 0.88GOPS/mm^2 Distributed Processor for Implantable Brain Machine Interfaces},
url = {http://dx.doi.org/10.1109/BioCAS.2016.7833806},
year = {2017}
}

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TY  - CPAPER
AB  - This paper presents a scalable architecture in 0.18u m CMOS for implantable brain machine interfaces (BMI) that enables micro controller flexibility for data analysis at the sensor interface. By introducing more generic computational capabilities the system is capable of high level adaptive function to potentially improve the long term efficacy of invasive implants. This topology features a compact ultra low power distributedprocessor that supports 64-channel neural recording system on chip (SOC) with a computational efficiency of 2.7uW/MIPS with a total chip area of 1.37mm2. This configuration executes 1024 instructions on each core at 20MHz to consolidate full spectrum high precision recordings from 4 analogue channels for filtering, spike detection, and feature extraction in the digital domain.
AU  - Leene,L
AU  - Constandinou,TG
DO  - 10.1109/BioCAS.2016.7833806
EP  - 363
PB  - IEEE
PY  - 2017///
SP  - 360
TI  - A 2.7uW/Mips, 0.88GOPS/mm^2 Distributed Processor for Implantable Brain Machine Interfaces
UR  - http://dx.doi.org/10.1109/BioCAS.2016.7833806
UR  - http://hdl.handle.net/10044/1/40782
ER  - 

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