Context-aware convolutional neural network over distributed system in collaborative computing

Jinhang Choi, Zeinab Hakimi, Philip W. Shin, John Morgan Sampson, Vijaykrishnan Narayanan

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

1 Citation (Scopus)

Abstract

As the computing power of end-point devices grows, there has been interest in developing distributed deep neural networks specifically for hierarchical inference deployments on multi-sensor systems. However, as the existing approaches rely on latent parameters trained by machine learning, it is difficult to preemptively select front-end deep features across sensors, or understand individual feature's relative importance for systematic global inference. In this paper, we propose multi-view convolutional neural networks exploiting likelihood estimation. Proof-of-concept experiments show that our likelihood-based context selection and weighted averaging collaboration scheme can decrease an endpoint's communication and energy costs by a factor of 3×, while achieving high accuracy comparable to the original aggregation approaches.

Original languageEnglish (US)
Title of host publicationProceedings of the 56th Annual Design Automation Conference 2019, DAC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450367257
DOIs
StatePublished - Jun 2 2019
Event56th Annual Design Automation Conference, DAC 2019 - Las Vegas, United States
Duration: Jun 2 2019Jun 6 2019

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference56th Annual Design Automation Conference, DAC 2019
CountryUnited States
CityLas Vegas
Period6/2/196/6/19

Fingerprint

Computer supported cooperative work
Sensor data fusion
Context-aware
Learning systems
Distributed Systems
Likelihood
Agglomeration
Neural Networks
Neural networks
Computing
Distributed Networks
Communication
Sensors
End point
Averaging
Costs
Aggregation
Machine Learning
High Accuracy
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

Cite this

Choi, J., Hakimi, Z., Shin, P. W., Sampson, J. M., & Narayanan, V. (2019). Context-aware convolutional neural network over distributed system in collaborative computing. In Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019 [a211] (Proceedings - Design Automation Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3316781.3317792
Choi, Jinhang ; Hakimi, Zeinab ; Shin, Philip W. ; Sampson, John Morgan ; Narayanan, Vijaykrishnan. / Context-aware convolutional neural network over distributed system in collaborative computing. Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - Design Automation Conference).
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Choi, J, Hakimi, Z, Shin, PW, Sampson, JM & Narayanan, V 2019, Context-aware convolutional neural network over distributed system in collaborative computing. in Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019., a211, Proceedings - Design Automation Conference, Institute of Electrical and Electronics Engineers Inc., 56th Annual Design Automation Conference, DAC 2019, Las Vegas, United States, 6/2/19. https://doi.org/10.1145/3316781.3317792

Context-aware convolutional neural network over distributed system in collaborative computing. / Choi, Jinhang; Hakimi, Zeinab; Shin, Philip W.; Sampson, John Morgan; Narayanan, Vijaykrishnan.

Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. a211 (Proceedings - Design Automation Conference).

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

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Choi J, Hakimi Z, Shin PW, Sampson JM, Narayanan V. Context-aware convolutional neural network over distributed system in collaborative computing. In Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. a211. (Proceedings - Design Automation Conference). https://doi.org/10.1145/3316781.3317792