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Adaptive parallel execution of deep neural networks on heterogeneous edge devices

Authors:

Mohammad Hossein Samavatian,

Saikat Majumdar,

Radu TeodorescuAuthors Info & Claims

SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

Pages 195 - 208

https://doi.org/10.1145/3318216.3363312

Published: 07 November 2019 Publication History

Abstract

New applications such as smart homes, smart cities, and autonomous vehicles are driving an increased interest in deploying machine learning on edge devices. Unfortunately, deploying deep neural networks (DNNs) on resource-constrained devices presents significant challenges. These workloads are computationally intensive and often require cloud-like resources. Prior solutions attempted to address these challenges by either introducing more design efforts or by relying on cloud resources for assistance.

In this paper, we propose a runtime adaptive convolutional neural network (CNN) acceleration framework that is optimized for heterogeneous Internet of Things (IoT) environments. The framework leverages spatial partitioning techniques through fusion of the convolution layers and dynamically selects the optimal degree of parallelism according to the availability of computational resources, as well as network conditions. Our evaluation shows that our framework outperforms state-of-art approaches by improving the inference speed and reducing communication costs while running on wirelessly-connected Raspberry-Pi3 devices. Experimental evaluation shows up to 1.9x ~ 3.7x speedup using 8 devices for three popular CNN models.

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Dai PHan BLi KXu XXing HLiu K(2025)Joint Optimization of Device Placement and Model Partitioning for Cooperative DNN Inference in Heterogeneous Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2024.345779324:1(210-226)Online publication date: Jan-2025
https://doi.org/10.1109/TMC.2024.3457793
Guo XJiang QPimentel AStefanov T(2025)Model and system robustness in distributed CNN inference at the edgeIntegration10.1016/j.vlsi.2024.102299100(102299)Online publication date: Jan-2025
https://doi.org/10.1016/j.vlsi.2024.102299
Zhang MShi HMa R(2024)Edge-Distributed IoT Services Assist the Economic Sustainability of LEO Satellite Constellation ConstructionSustainability10.3390/su1604159916:4(1599)Online publication date: 14-Feb-2024
https://doi.org/10.3390/su16041599
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Index Terms

Adaptive parallel execution of deep neural networks on heterogeneous edge devices
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded hardware
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
  2. Parallel computing methodologies
    1. Parallel algorithms
      1. Massively parallel algorithms

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Information & Contributors

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Published In

cover image ACM Conferences

SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

November 2019

455 pages

ISBN:9781450367332

DOI:10.1145/3318216

General Chairs:
Songqing Chen
George Mason University
,
Ryokichi Onishi
Toyota
,
Program Chairs:
Ganesh Ananthanarayanan
Microsoft Research
,
Qun Li
College of William & Mary

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 November 2019

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SEC '19

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SIGMOBILE

SEC '19: The Fourth ACM/IEEE Symposium on Edge Computing

November 7 - 9, 2019

Virginia, Arlington

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SEC '19 Paper Acceptance Rate 20 of 59 submissions, 34%;

Overall Acceptance Rate 40 of 100 submissions, 40%

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Cited By

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https://doi.org/10.1109/TMC.2024.3457793
Guo XJiang QPimentel AStefanov T(2025)Model and system robustness in distributed CNN inference at the edgeIntegration10.1016/j.vlsi.2024.102299100(102299)Online publication date: Jan-2025
https://doi.org/10.1016/j.vlsi.2024.102299
Zhang MShi HMa R(2024)Edge-Distributed IoT Services Assist the Economic Sustainability of LEO Satellite Constellation ConstructionSustainability10.3390/su1604159916:4(1599)Online publication date: 14-Feb-2024
https://doi.org/10.3390/su16041599
Wang JChen CLi SWang CCao XYang L(2024)Researching the CNN Collaborative Inference Mechanism for Heterogeneous Edge DevicesSensors10.3390/s2413417624:13(4176)Online publication date: 27-Jun-2024
https://doi.org/10.3390/s24134176
Hu XLi HZhang HWu BMa LWen XGao J(2024)Empirically informed convolutional neural network model for logging curve calibrationGEOPHYSICS10.1190/geo2022-0696.189:2(D139-D148)Online publication date: 14-Feb-2024
https://doi.org/10.1190/geo2022-0696.1
Li LLi YTan H(2024)DeepDecompose: A Distributed inference framework for CNN on GPU-equipped Edge ClustersProceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things10.1145/3670105.3670200(540-544)Online publication date: 24-May-2024
https://dl.acm.org/doi/10.1145/3670105.3670200
Chen YLuo TFang WXiong N(2024)EdgeCI: Distributed Workload Assignment and Model Partitioning for CNN Inference on Edge ClustersACM Transactions on Internet Technology10.1145/365604124:2(1-24)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.1145/3656041
Lee JBahk IKim HJeong SLee SMin D(2024)An Autonomous Parallelization of Transformer Model Inference on Heterogeneous Edge DevicesProceedings of the 38th ACM International Conference on Supercomputing10.1145/3650200.3656628(50-61)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3650200.3656628
Yang XXu ZQi QWang JSun HLiao JGuo S(2024)PICO: Pipeline Inference Framework for Versatile CNNs on Diverse Mobile DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2023.326511123:4(2712-2730)Online publication date: Apr-2024
https://doi.org/10.1109/TMC.2023.3265111
Hou XGuan YChoi NHan T(2024)BPS: Batching, Pipelining, Surgeon of Continuous Deep Inference on Collaborative Edge IntelligenceIEEE Transactions on Cloud Computing10.1109/TCC.2024.339961612:3(830-843)Online publication date: Jul-2024
https://doi.org/10.1109/TCC.2024.3399616
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