Sparse Optimization for Green Edge AI Inference
- URL: http://arxiv.org/abs/2002.10080v2
- Date: Fri, 13 Mar 2020 13:11:12 GMT
- Title: Sparse Optimization for Green Edge AI Inference
- Authors: Xiangyu Yang, Sheng Hua, Yuanming Shi, Hao Wang, Jun Zhang, Khaled B.
Letaief
- Abstract summary: We present a joint inference task selection and downlink beamforming strategy to achieve energy-efficient edge AI inference.
By exploiting the inherent connections between the set of task selection and group sparsity transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem.
We establish the global convergence analysis and provide the ergodic worst-case convergence rate for this algorithm.
- Score: 28.048770388766716
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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