# Energy Optimization of Multi-task DNN Inference in MEC-assisted XR Devices: A Lyapunov-Guided Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2501.02572 · HTML: https://mlsanity.com/paper/2501.02572/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 5 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 13 of 13)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [Communication-Efficient Edge AI: Algorithms and Systems](https://mlsanity.com/paper/2002.09668/) (2020)
- [Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks](https://mlsanity.com/paper/2011.08430/) (2020)
- [Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks](https://mlsanity.com/paper/2010.01370/) (2020)
- [Standardization of Extended Reality (XR) over 5G and 5G-Advanced 3GPP New Radio](https://mlsanity.com/paper/2203.02242/) (2022)
- [Joint Optimization of Video-based AI Inference Tasks in MEC-assisted Augmented Reality Systems](https://mlsanity.com/paper/2301.01010/) (2023)
- [D3PG: Dirichlet DDPG for Task Partitioning and Offloading with Constrained Hybrid Action Space in Mobile Edge Computing](https://mlsanity.com/paper/2112.09328/) (2021)
- [Quality of Experience Oriented Cross-layer Optimization for Real-time XR Video Transmission](https://mlsanity.com/paper/2404.09905/) (2024)
- [End-to-End Delay Minimization based on Joint Optimization of DNN Partitioning and Resource Allocation for Cooperative Edge Inference](https://mlsanity.com/paper/2310.12937/) (2023)
- [Robust Dual-Modal Speech Keyword Spotting for XR Headsets](https://mlsanity.com/paper/2401.14978/) (2024)
- [Quality of Experience Optimization for Real-time XR Video Transmission with Energy Constraints](https://mlsanity.com/paper/2405.07689/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)

## Abstract (arXiv)

Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this paper, we developed a distributed queue model for multi-task DNN inference, addressing issues of resource competition and queue coupling. In response to the challenges posed by the high energy consumption and limited resources of XR devices, we designed a dual time-scale joint optimization strategy for model partitioning and resource allocation, formulated as a bi-level optimization problem. This strategy aims to minimize the total energy consumption of XR devices while ensuring queue stability and adhering to computational and communication resource constraints. To tackle this problem, we devised a Lyapunov-guided Proximal Policy Optimization algorithm, named LyaPPO. Numerical results demonstrate that the LyaPPO algorithm outperforms the baselines, achieving energy conservation of 24.79% to 46.14% under varying resource capacities. Specifically, the proposed algorithm reduces the energy consumption of XR devices by 24.29% to 56.62% compared to baseline algorithms.