# Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing

arXiv: https://arxiv.org/abs/2210.13464 · HTML: https://mlsanity.com/paper/2210.13464/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Early exit and edge inference, one of 12 directions in Efficient LLM inference and serving.
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Placed in

- Efficient LLM inference and serving / [Early exit and edge inference](https://mlsanity.com/inference/early-exit-and-edge-inference/)

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead](https://mlsanity.com/paper/2105.09121/) (2021)
- [Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks](https://mlsanity.com/paper/1705.00704/) (2017)
- [Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning](https://mlsanity.com/paper/2104.10461/) (2021)
- [Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing](https://mlsanity.com/paper/2207.11294/) (2022)
- [Empirical analysis of non-linear activation functions for Deep Neural Networks in classification tasks](https://mlsanity.com/paper/1710.11272/) (2017)

## Built on by (most cited first, 4 of 4)

- [Attention-based Feature Compression for CNN Inference Offloading in Edge Computing](https://mlsanity.com/paper/2211.13745/) (2022)
- [QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference](https://mlsanity.com/paper/2506.23934/) (2025)
- [Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications](https://mlsanity.com/paper/2211.13787/) (2022)
- [Edge AI as a Service with Coordinated Deep Neural Networks](https://mlsanity.com/paper/2401.00631/) (2024)

## Abstract (arXiv)

This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and Edge servers' available capacity, we use early-exit mechanism to terminate the computation earlier to meet the deadline of inference tasks. We design a reward function to trade off the communication, computation and inference accuracy, and formulate the offloading problem of CNN inference as a maximization problem with the goal of maximizing the average inference accuracy and throughput in long term. To solve the maximization problem, we propose a graph reinforcement learning-based early-exit mechanism (GRLE), which outperforms the state-of-the-art work, deep reinforcement learning-based online offloading (DROO) and its enhanced method, DROO with early-exit mechanism (DROOE), under different dynamic scenarios. The experimental results show that GRLE achieves the average accuracy up to 3.41x over graph reinforcement learning (GRL) and 1.45x over DROOE, which shows the advantages of GRLE for offloading decision-making in dynamic MEC.