Design and optimization of edge computing distributed neural processor for biomedical rehabilitation with sensor fusion

Kofi Otseidu, Tianyu Jia, Joshua Bryne, Levi J Hargrove, Jie Gu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Modern biomedical devices use sensor fusion techniques to improve the classification accuracy of motion intent of users for rehabilitation application. The design of motion classifier observes significant challenges due to the large number of channels and stringent communication latency requirement. This paper proposes an edge-computing distributed neural processor to effectively reduce the data traffic and physical wiring congestion. A special local and global networking architecture is introduced to significantly reduce traffic among multi-chips in edge computing. To optimize the design space of the features selected, a systematic design methodology is proposed. A novel mixed-signal feature extraction approach with assistance of neural network distortion recovery is also provided to significantly reduce the silicon area. A 12-channel 55nm CMOS test chip was implemented to demonstrate the proposed systematic design methodology. The measurement shows the test chip consumes only 20uW power, more than 10,000X less power than the current clinically used microprocessor and can perform edge-computing networking operation within 5ms time.

Original languageEnglish (US)
Title of host publication2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450359504
DOIs
StatePublished - Nov 5 2018
Event37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - San Diego, United States
Duration: Nov 5 2018Nov 8 2018

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152

Other

Other37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018
CountryUnited States
CitySan Diego
Period11/5/1811/8/18

Keywords

  • biomedical devices
  • inter-chip communication
  • low power edge processing
  • mixed signal feature extraction
  • neural network

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Otseidu, K., Jia, T., Bryne, J., Hargrove, L. J., & Gu, J. (2018). Design and optimization of edge computing distributed neural processor for biomedical rehabilitation with sensor fusion. In 2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers [a120] (IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3240765.3240794