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

arXiv: https://arxiv.org/abs/1812.06426 · HTML: https://mlsanity.com/paper/1812.06426/

## Key facts

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

## Built on by (most cited first, 20 of 20)

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- [PrivaScissors: Enhance the Privacy of Collaborative Inference through the Lens of Mutual Information](https://mlsanity.com/paper/2306.07973/) (2023)
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- [On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance](https://mlsanity.com/paper/2403.17154/) (2024)
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- [MTL-Split: Multi-Task Learning for Edge Devices using Split Computing](https://mlsanity.com/paper/2407.05982/) (2024)
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- [LimitNet: Progressive, Content-Aware Image Offloading for Extremely Weak Devices & Networks](https://mlsanity.com/paper/2504.13736/) (2025)
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- [Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions](https://mlsanity.com/paper/2510.22909/) (2025)

## 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.