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BlastNet: Exploiting Duo-Blocks for Cross-Processor Real-Time DNN Inference

Authors:

Guoliang XingAuthors Info & Claims

SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

Pages 91 - 105

https://doi.org/10.1145/3560905.3568520

Published: 24 January 2023 Publication History

Abstract

In recent years, Deep Neural Network (DNN) has been increasingly adopted by a wide range of time-critical applications running on edge platforms with heterogeneous multiprocessors. To meet the stringent timing requirements of these applications, heterogeneous CPU and GPU resources must be efficiently utilized for the inference of multiple DNN models. Such a cross-processor real-time DNN inference paradigm poses major challenges due to the inherent performance imbalance among different processors and the lack of real-time support for cross-processor inference from existing deep learning frameworks. In this work, we propose a new system named BlastNet that exploits duo-block - a new model inference abstraction to support highly efficient cross-processor real-time DNN inference. Each duo-block has a dual model structure, enabling efficient fine-grained inference alternatively across different processors. BlastNet employs a novel block-level Neural Architecture Search (NAS) technique to generate duo-blocks, which accounts for computing characteristics and communication overhead. The duo-blocks are optimized at design time and then dynamically scheduled to achieve high resource utilization of heterogeneous CPU and GPU at runtime. BlastNet is implemented on an indoor autonomous driving platform and three popular edge platforms. Extensive results show that BlastNet achieves 35.07 % less deadline missing rate with a mere 1.63% of model accuracy loss.

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Cited By

Lee JJung CKim JCha HGanesan DLane NShi W(2024)Panopticus: Omnidirectional 3D Object Detection on Resource-constrained Edge DevicesProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3690688(1207-1221)Online publication date: 4-Dec-2024
https://dl.acm.org/doi/10.1145/3636534.3690688
Qi HRen FWang LJiang PWan SDeng X(2024)Multi-Compression Scale DNN Inference Acceleration based on Cloud-Edge-End CollaborationACM Transactions on Embedded Computing Systems10.1145/363470423:1(1-25)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3634704
Zhang ZZhao YLi HLin CLiu J(2024)DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative InferenceIEEE Transactions on Mobile Computing10.1109/TMC.2024.335721823:10(9042-9059)Online publication date: Oct-2024
https://doi.org/10.1109/TMC.2024.3357218
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Index Terms

BlastNet: Exploiting Duo-Blocks for Cross-Processor Real-Time DNN Inference
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
  2. Real-time systems
    1. Real-time system architecture
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Concurrent computing methodologies

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cover image ACM Conferences

SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

November 2022

1280 pages

ISBN:9781450398862

DOI:10.1145/3560905

General Chairs:
Jeremy Gummeson
University of Massachusetts Amherst
,
Sunghoon Ivan Lee
University of Massachusetts Amherst
,
Program Chairs:
Jie Gao
Rutgers University
,
Guoliang Xing
The Chinese University of Hong Kong

Copyright © 2022 ACM.

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Published: 24 January 2023

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SenSys '22

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SenSys '22: The 20th ACM Conference on Embedded Networked Sensor Systems

November 6 - 9, 2022

Massachusetts, Boston

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SenSys '22 Paper Acceptance Rate 52 of 187 submissions, 28%;

Overall Acceptance Rate 174 of 867 submissions, 20%

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Cited By

Lee JJung CKim JCha HGanesan DLane NShi W(2024)Panopticus: Omnidirectional 3D Object Detection on Resource-constrained Edge DevicesProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3690688(1207-1221)Online publication date: 4-Dec-2024
https://dl.acm.org/doi/10.1145/3636534.3690688
Qi HRen FWang LJiang PWan SDeng X(2024)Multi-Compression Scale DNN Inference Acceleration based on Cloud-Edge-End CollaborationACM Transactions on Embedded Computing Systems10.1145/363470423:1(1-25)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3634704
Zhang ZZhao YLi HLin CLiu J(2024)DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative InferenceIEEE Transactions on Mobile Computing10.1109/TMC.2024.335721823:10(9042-9059)Online publication date: Oct-2024
https://doi.org/10.1109/TMC.2024.3357218
Wang KCao JZhou ZLi Z(2024)SwapNet: Efficient Swapping for DNN Inference on Edge AI Devices Beyond the Memory BudgetIEEE Transactions on Mobile Computing10.1109/TMC.2024.335576423:9(8935-8950)Online publication date: Sep-2024
https://doi.org/10.1109/TMC.2024.3355764
Bansal AYuhas MEaswaran A(2024)Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment2024 IEEE 30th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)10.1109/RTCSA62462.2024.00015(37-42)Online publication date: 21-Aug-2024
https://doi.org/10.1109/RTCSA62462.2024.00015
Lin CLiu J(2024)COS: Cross-Processor Operator Scheduling for Multi-Tenant Deep Learning Inference2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)10.1109/IWQoS61813.2024.10682900(1-10)Online publication date: 19-Jun-2024
https://doi.org/10.1109/IWQoS61813.2024.10682900
Ye SDu JZeng LOu WChu XLu YChen X(2024)Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer InferenceIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621342(1001-1010)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621342
Moosmann JMüller HZimmerman NRutishauser GBenini LMagno M(2024)Flexible and Fully Quantized Lightweight TinyissimoYOLO for Ultra-Low-Power Edge SystemsIEEE Access10.1109/ACCESS.2024.340487812(75093-75107)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3404878
Kwon YChauhan JJia HVenieris SMascolo CEskicioglu RHuang PPatwari N(2023)LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing PlatformsProceedings of the 21st ACM Conference on Embedded Networked Sensor Systems10.1145/3625687.3625804(138-151)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3625687.3625804
Zhao ZLing NGuan NXing GEskicioglu RHuang PPatwari N(2023)Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPUProceedings of the 21st ACM Conference on Embedded Networked Sensor Systems10.1145/3625687.3625789(97-110)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3625687.3625789
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