A 2.7uW/Mips, 0.88GOPS/mm^2 Distributed Processor for Implantable Brain Machine Interfaces
File(s)PID4427085.pdf (1.41 MB)
Accepted version
Author(s)
Leene, L
Constandinou, TG
Type
Conference Paper
Abstract
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 distributed
processor 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.
processor 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.
Date Issued
2017-01-26
Date Acceptance
2016-08-10
Citation
2017, pp.360-363
Publisher
IEEE
Start Page
360
End Page
363
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/K015060/1
RES/0560/7386 & EFXD12018
EP/M020975/1
Source
IEEE Biomedical Circuits and Systems (BioCAS) Conference
Subjects
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Biomedical
Engineering, Electrical & Electronic
Computer Science
Engineering
ARCHITECTURE
Publication Status
Published
Start Date
2016-10-17
Finish Date
2016-10-19
Coverage Spatial
Shanghai, China