research-article

Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection

Authors:

Vicent Sanz Marco,

Yehia ElkhatibAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 19, Issue 1

Article No.: 2, Pages 1 - 28

https://doi.org/10.1145/3371154

Published: 06 February 2020 Publication History

Abstract

Deep neural networks (DNNs) are becoming a key enabling technique for many application domains. However, on-device inference on battery-powered, resource-constrained embedding systems is often infeasible due to prohibitively long inferencing time and resource requirements of many DNNs. Offloading computation into the cloud is often unacceptable due to privacy concerns, high latency, or the lack of connectivity. Although compression algorithms often succeed in reducing inferencing times, they come at the cost of reduced accuracy.

This article presents a new, alternative approach to enable efficient execution of DNNs on embedded devices. Our approach dynamically determines which DNN to use for a given input by considering the desired accuracy and inference time. It employs machine learning to develop a low-cost predictive model to quickly select a pre-trained DNN to use for a given input and the optimization constraint. We achieve this first by offline training a predictive model and then using the learned model to select a DNN model to use for new, unseen inputs. We apply our approach to two representative DNN domains: image classification and machine translation. We evaluate our approach on a Jetson TX2 embedded deep learning platform and consider a range of influential DNN models including convolutional and recurrent neural networks. For image classification, we achieve a 1.8x reduction in inference time with a 7.52% improvement in accuracy over the most capable single DNN model. For machine translation, we achieve a 1.34x reduction in inference time over the most capable single model with little impact on the quality of translation.

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Index Terms

Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded software
2. Computing methodologies
  1. Machine learning
  2. Parallel computing methodologies

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 19, Issue 1

January 2020

185 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/3382497

Editor:
Sandeep K. Shukla
Indian Institute of Technology, India

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Copyright © 2020 ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 06 February 2020

Accepted: 01 October 2019

Revised: 01 August 2019

Received: 01 April 2019

Published in TECS Volume 19, Issue 1

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https://dl.acm.org/doi/10.1145/3657283
Bala ARashid RIsmail IOliva DMuhammad NSait SAl-Utaibi KAmosa TMemon K(2024)Artificial intelligence and edge computing for machine maintenance-reviewArtificial Intelligence Review10.1007/s10462-024-10748-957:5Online publication date: 15-Apr-2024
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