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Deep Reinforcement Learning Based Resource Management for DNN Inference in IIoT | IEEE Conference Publication | IEEE Xplore

Deep Reinforcement Learning Based Resource Management for DNN Inference in IIoT


Abstract:

In this paper, we investigate the joint task assignment and resource allocation for deep neural network (DNN) inference in the device-edge-cloud based industrial Internet...Show More

Abstract:

In this paper, we investigate the joint task assignment and resource allocation for deep neural network (DNN) inference in the device-edge-cloud based industrial Internet of things (IIoT) networks. To efficiently orchestrate the limited spectrum and computing resources in IIoT networks for massive DNN inference tasks, a resource management problem is formulated with the objective of maximizing the average inference accuracy while satisfying the quality-of-service of DNN inference tasks. Considering the strict delay requirements of inference tasks, we transform the formulated problem into a Markov decision process, and propose a deep deterministic policy gradient based learning algorithm to obtain the solution rapidly. Simulation results show that the proposed algorithm can achieve high average inference accuracy.
Date of Conference: 07-11 December 2020
Date Added to IEEE Xplore: 25 January 2021
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Conference Location: Taipei, Taiwan

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