Communication-Computation Efficient Device-Edge Co-Inference via AutoML

Xinjie Zhang, Jiawei Shao, Yuyi Mao, Jun Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Device-edge co-inference, which partitions a deep neural network between a resource-constrained mobile device and an edge server, recently emerges as a promising paradigm to support intelligent mobile applications. To accelerate the in-ference process, on-device model sparsification and intermediate feature compression are regarded as two prominent techniques. However, as the on-device model sparsity level and intermediate feature compression ratio have direct impacts on computation workload and communication overhead respectively, and both of them affect the inference accuracy, finding the optimal values of these hyper-parameters brings a major challenge due to the large search space. In this paper, we endeavor to develop an efficient algorithm to determine these hyper-parameters. By selecting a suitable model split point and a pair of encoder/decoder for the intermediate feature vector, this problem is casted as a sequential decision problem, for which, a novel automated machine learning (AutoML) framework is proposed based on deep reinforcement learning (DRL). Experiment results on an image classification task demonstrate the effectiveness of the proposed framework in achieving a better communication-computation trade-off and significant inference speedup against various baseline schemes.

Original languageEnglish
Title of host publication2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728181042
Publication statusPublished - Dec 2021
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: 7 Dec 202111 Dec 2021

Publication series

Name2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings


Conference2021 IEEE Global Communications Conference, GLOBECOM 2021


  • automated machine learning (AutoML)
  • communication-computation trade-off
  • deep neural network (DNN)
  • deep reinforce-ment learning (DRL)
  • Device-edge co-inference

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics


Dive into the research topics of 'Communication-Computation Efficient Device-Edge Co-Inference via AutoML'. Together they form a unique fingerprint.

Cite this