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demonstration

Edge Acceleration of Deep Neural Networks (demo)

Published: 12 June 2019 Publication History

Abstract

Running deep learning algorithms at the edge is a necessity in many industrial use-cases, especially in applications that use robots and drones in disaster recovery, surveillance, oil & gas operations etc. Current state of the art deep learning algorithms are extremely efficient in analysing image, audio, video and other time-series signals. However, their performance degrades considerably on constrained edge devices. In this demo, we show how standard pretrained CNN (Convolutional Neural Network) models can be partitioned for efficient parallel execution between constrained devices and also achieve real-time response.

References

[1]
Andrew G. Howard, MenglongZhu, Bo Chen, Dmitry Kalenichenko,Weijun Wang, Tobias Weyand,Marco Andreetto, and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networksfor Mobile Vision Applications. CoRR, Vol. abs/1704.04861 (2017). arxiv: 1704.04861 http://arxiv.org/abs/1704.04861
[2]
Forrest N. Iandola,Matthew W. Moskewicz, Khalid Ashraf,Song Han, William J. Dally, andKurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewerparameters and textless1MB model size. CoRR, Vol. abs/1602.07360 (2016). arxiv: 1602.07360 http://arxiv.org/abs/1602.07360

Cited By

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  • (2021)Artificial Intelligence and Machine Learning in ManufacturingDigital Twin – Fundamental Concepts to Applications in Advanced Manufacturing10.1007/978-3-030-81815-9_6(337-412)Online publication date: 13-Aug-2021

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cover image ACM Conferences
MobiSys '19: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
June 2019
736 pages
ISBN:9781450366618
DOI:10.1145/3307334
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2019

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Author Tags

  1. acceleration
  2. computing
  3. dnn
  4. edge
  5. latency
  6. network
  7. partitioning

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  • Demonstration

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MobiSys '19
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Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

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  • (2021)Artificial Intelligence and Machine Learning in ManufacturingDigital Twin – Fundamental Concepts to Applications in Advanced Manufacturing10.1007/978-3-030-81815-9_6(337-412)Online publication date: 13-Aug-2021

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