research-article

Edge segmentation: empowering mobile telemedicine with compressed cellular neural networks

Authors:

Xiaobo Sharon Hu,

Yiyu ShiAuthors Info & Claims

ICCAD '17: Proceedings of the 36th International Conference on Computer-Aided Design

Pages 880 - 887

Published: 13 November 2017 Publication History

Abstract

With the need for increased care and welfare of the rapidly aging population, mobile telemedicine is becoming popular for providing remote health care to increase the quality of life. Recently, image analysis is being actively applied for medical diagnosis and treatment, in which image segmentation is of the fundamental importance for other image processing such as visualization and detection. However, given the tasks challenges in transmitting large volume of high-resolution images and the real-time constraints that are commonly present for mobile telemedicine, image segmentation is best done at the "edge", i.e., locally so that only segmentation results are communicated. A powerful approach to medical image segmentation is cellular neural network (CeNN), which can achieve very high accuracy through proper training. However, CeNNs typically involve extensive computations in a recursive manner. As an example, to simply process an image of 1920x1080 pixels requires 4-8 Giga floating point multiplications (for 3x3 templates and 50-100 iterations), which needs to be done in a timely manner for real-time medical image segmentation. Such a demand is too high for most low power mobile computing platforms in IoTs. This paper presents a compressed CeNN framework for computation reduction in CeNNs, which is the first in the literature. It involves various techniques such as early exit and parameter quantization, which significantly reduces computation demands while maintaining an acceptable performance.

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Cited By

Chen WQiu HZhuang JZhang CHu YLu QWang TShi YHuang MXu X(2021)Quantization of Deep Neural Networks for Accurate Edge ComputingACM Journal on Emerging Technologies in Computing Systems10.1145/345121117:4(1-11)Online publication date: 30-Jun-2021
https://dl.acm.org/doi/10.1145/3451211
Zhang XPatterson CLiu YYang CXue CHu J(2020)Low Overhead Online Data Flow Tracking for Intermittently Powered Non-Volatile FPGAsACM Journal on Emerging Technologies in Computing Systems10.1145/337139216:3(1-20)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1145/3371392
Lou QPan CMcGuinness JHorvath ANaeemi ANiemier MHu X(2019)A Mixed Signal Architecture for Convolutional Neural NetworksACM Journal on Emerging Technologies in Computing Systems10.1145/330411015:2(1-26)Online publication date: 26-Mar-2019
https://dl.acm.org/doi/10.1145/3304110
Show More Cited By

Edge segmentation: empowering mobile telemedicine with compressed cellular neural networks
1. Applied computing
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems

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Published In

cover image ACM Conferences

ICCAD '17: Proceedings of the 36th International Conference on Computer-Aided Design

November 2017

1077 pages

Conference Chair:
Sri Parameswaran
GENERAL CHAIR

Sponsors

CEDA: Council on Electronic Design Automation
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CAS: Circuits & Systems

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IEEE-EDS: Electronic Devices Society

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IEEE Press

Publication History

Published: 13 November 2017

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ICCAD '17

Sponsor:

CEDA
SIGDA
IEEE-CAS

ICCAD '17: IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN

November 13 - 16, 2017

California, Irvine

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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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Cited By

Chen WQiu HZhuang JZhang CHu YLu QWang TShi YHuang MXu X(2021)Quantization of Deep Neural Networks for Accurate Edge ComputingACM Journal on Emerging Technologies in Computing Systems10.1145/345121117:4(1-11)Online publication date: 30-Jun-2021
https://dl.acm.org/doi/10.1145/3451211
Zhang XPatterson CLiu YYang CXue CHu J(2020)Low Overhead Online Data Flow Tracking for Intermittently Powered Non-Volatile FPGAsACM Journal on Emerging Technologies in Computing Systems10.1145/337139216:3(1-20)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1145/3371392
Lou QPan CMcGuinness JHorvath ANaeemi ANiemier MHu X(2019)A Mixed Signal Architecture for Convolutional Neural NetworksACM Journal on Emerging Technologies in Computing Systems10.1145/330411015:2(1-26)Online publication date: 26-Mar-2019
https://dl.acm.org/doi/10.1145/3304110
Jiang WZhang XSha EZhuge QYang LShi YHu JBazargan KNeuendorffer S(2019)XFERProceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays10.1145/3289602.3293988(305-305)Online publication date: 20-Feb-2019
https://dl.acm.org/doi/10.1145/3289602.3293988
Xu XLu QWang THu YZhuo CLiu JShi Y(2018)Efficient Hardware Implementation of Cellular Neural Networks with Incremental Quantization and Early ExitACM Journal on Emerging Technologies in Computing Systems10.1145/326481714:4(1-20)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1145/3264817

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