# Calibration-Aided Edge Inference Offloading via Adaptive Model Partitioning of Deep Neural Networks

arXiv: https://arxiv.org/abs/2010.16335 · HTML: https://mlsanity.com/paper/2010.16335/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [CEEBERT: Cross-Domain Inference in Early Exit BERT](https://mlsanity.com/paper/2405.15039/) (2024)
- [Unsupervised Early Exit in DNNs with Multiple Exits](https://mlsanity.com/paper/2209.09480/) (2022)
- [Early-exit deep neural networks for distorted images: providing an efficient edge offloading](https://mlsanity.com/paper/2108.09343/) (2021)
- [The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks](https://mlsanity.com/paper/2505.09214/) (2025)
- [BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts](https://mlsanity.com/paper/2502.00745/) (2025)
- [A Survey on Deep Neural Network Partition over Cloud, Edge and End Devices](https://mlsanity.com/paper/2304.10020/) (2023)
- [Improving Image-recognition Edge Caches with a Generative Adversarial Network](https://mlsanity.com/paper/2202.05929/) (2022)

## Abstract (arXiv)

Mobile devices can offload deep neural network (DNN)-based inference to the cloud, overcoming local hardware and energy limitations. However, offloading adds communication delay, thus increasing the overall inference time, and hence it should be used only when needed. An approach to address this problem consists of the use of adaptive model partitioning based on early-exit DNNs. Accordingly, the inference starts at the mobile device, and an intermediate layer estimates the accuracy: If the estimated accuracy is sufficient, the device takes the inference decision; Otherwise, the remaining layers of the DNN run at the cloud. Thus, the device offloads the inference to the cloud only if it cannot classify a sample with high confidence. This offloading requires a correct accuracy prediction at the device. Nevertheless, DNNs are typically miscalibrated, providing overconfident decisions. This work shows that the employment of a miscalibrated early-exit DNN for offloading via model partitioning can significantly decrease inference accuracy. In contrast, we argue that implementing a calibration algorithm prior to deployment can solve this problem, allowing for more reliable offloading decisions.