Sparse optimization for green edge ai inference

Xiang Yu Yang, Sheng Hua, Yuan Ming Shi, Hao Wang, Jun Zhang, Khaled B. Letaief

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


With the rapid upsurge of deep learning tasks at the network edge, effective edge artificial intelligence (AI) inference becomes critical to provide low-latency intelligent services for mobile users via leveraging the edge computing capability. In such scenarios, energy efficiency becomes a primary concern. In this paper, we present a joint inference task selection and downlink beamforming strategy to achieve energy-efficient edge AI inference through minimizing the overall power consumption consisting of both computation and transmission power consumption, yielding a mixed combinatorial optimization problem. By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem. To solve this challenging problem, we propose a log-sum function based three-stage approach. By adopting the log-sum function to enhance the group sparsity, a proximal iteratively reweighted algorithm is developed. Furthermore, we establish the global convergence analysis and provide the ergodic worst-case convergence rate for this algorithm. Simulation results will demonstrate the effectiveness of the proposed approach for improving energy efficiency in edge AI inference systems.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalJournal of Communications and Information Networks
Issue number1
Publication statusPublished - 1 Mar 2020


  • AI
  • Cooperative transmission
  • Edge inference
  • Energy efficiency
  • Group sparse beamforming
  • Proximal iteratively reweighted algorithm

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Networks and Communications


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