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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2404.19209 (cs)
[Submitted on 30 Apr 2024]

Title:AdaOper: Energy-efficient and Responsive Concurrent DNN Inference on Mobile Devices

Authors:Zheng Lin, Bin Guo, Sicong Liu, Wentao Zhou, Yasan Ding, Yu Zhang, Zhiwen Yu
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Abstract:Deep neural network (DNN) has driven extensive applications in mobile technology. However, for long-running mobile apps like voice assistants or video applications on smartphones, energy efficiency is critical for battery-powered devices. The rise of heterogeneous processors in mobile devices today has introduced new challenges for optimizing energy efficiency. Our key insight is that partitioning computations across different processors for parallelism and speedup doesn't necessarily correlate with energy consumption optimization and may even increase it. To address this, we present AdaOper, an energy-efficient concurrent DNN inference system. It optimizes energy efficiency on mobile heterogeneous processors while maintaining responsiveness. AdaOper includes a runtime energy profiler that dynamically adjusts operator partitioning to optimize energy efficiency based on dynamic device conditions. We conduct preliminary experiments, which show that AdaOper reduces energy consumption by 16.88% compared to the existing concurrent method while ensuring real-time performance.
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as: arXiv:2404.19209 [cs.DC]
  (or arXiv:2404.19209v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.2404.19209

Submission history

From: Zheng Lin [view email]
[v1] Tue, 30 Apr 2024 02:22:31 UTC (258 KB)
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