mlsanityData to 14 Oct 2026

Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge

arXiv 1812.06426 ↗First posted 16 Dec 2018cs.DC cs.AI cs.CVcited by 20

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Abstract (arXiv)

Recently, deep neural networks (DNNs) have been widely applied in mobile intelligent applications. The inference for the DNNs is usually performed in the cloud. However, it leads to a large overhead of transmitting data via wireless network. In this paper, we demonstrate the advantages of the cloud-edge collaborative inference with quantization. By analyzing the characteristics of layers in DNNs, an auto-tuning neural network quantization framework for collaborative inference is proposed. We study the effectiveness of mixed-precision collaborative inference of state-of-the-art DNNs by using ImageNet dataset. The experimental results show that our framework can generate reasonable network partitions and reduce the storage on mobile devices with trivial loss of accuracy.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Dec 2018.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 1; 2024: 5; 2025: 4.

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  1. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 175
  2. 2021Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challengescited by 115
  3. 2021Pareto-Optimal Quantized ResNet Is Mostly 4-bitcited by 26
  4. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  5. 2022SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systemscited by 14
  6. 2022Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learningcited by 12
  7. 2022Resource-Constrained Edge AI with Early Exit Predictioncited by 11
  8. 2022I-SPLIT: Deep Network Interpretability for Split Computingcited by 7
  9. 2023PrivaScissors: Enhance the Privacy of Collaborative Inference through the Lens of Mutual Informationcited by 4
  10. 2022Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systemscited by 3
  11. 2022Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computingcited by 3
  12. 2024On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performancecited by 3
8 more
  1. 2025Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantizationcited by 3
  2. 2024MTL-Split: Multi-Task Learning for Edge Devices using Split Computingcited by 2
  3. 2024Enhancing Split Computing and Early Exit Applications through Predefined Sparsitycited by 1
  4. 2024On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performancecited by 1
  5. 2025LimitNet: Progressive, Content-Aware Image Offloading for Extremely Weak Devices & Networkscited by 1
  6. 2024Combining Cloud and Mobile Computing for Machine Learningcited by 0
  7. 2025DCentNet: Decentralized Multistage Biomedical Signal Classification using Early Exitscited by 0
  8. 2025Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directionscited by 0