Papers › Context-aware deep model compression for edge cloud computing
Context-aware deep model compression for edge cloud computing
Lingdong Wang, Liyao Xiang, Jiayu Xu, Jiaju Chen, Xing Zhao, Dixi Yao, Xinbing Wang, Baochun Li
While deep neural networks (DNNs) have led to a paradigm shift, its exorbitant computational requirement has always been a roadblock in its deployment to the edge, such as wearable devices and smartphones. Hence a hybrid edge-cloud computational framework is proposed to transfer part of the computation to the cloud, by naively partitioning the DNN operations under the constant network condition assumption. However, real-world network state varies greatly depending on the context, and DNN partitioning only has limited strategy space. In this paper, we explore the structural flexibility of DNN to fit the edge model to varying network contexts and different deployment platforms. Specifically, we designed a reinforcement learning-based decision engine to search for model transformation strategies in response to a combined objective of model accuracy and computation latency. The engine generates a context-aware model tree so that the DNN can decide the model branch to switch to at runtime. By the emulation and field experimental results, our approach enjoys a 30% − 50% latency reduction while retaining the model accuracy.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | Context-Aware DNN tree | Percentage correct | 92.01 | #187 of 265 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections