# I-SplitEE: Image classification in Split Computing DNNs with Early Exits

arXiv: https://arxiv.org/abs/2401.10541 · HTML: https://mlsanity.com/paper/2401.10541/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference](https://mlsanity.com/paper/2004.12993/) (2020)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
- [SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud](https://mlsanity.com/paper/2008.06402/) (2020)
- [BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems](https://mlsanity.com/paper/1910.14315/) (2019)
- [BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1902.01000/) (2019)
- [Towards Efficient NLP: A Standard Evaluation and A Strong Baseline](https://mlsanity.com/paper/2110.07038/) (2021)
- [LGViT: Dynamic Early Exiting for Accelerating Vision Transformer](https://mlsanity.com/paper/2308.00255/) (2023)
- [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)

## Built on by (most cited first, 9 of 9)

- [CEEBERT: Cross-Domain Inference in Early Exit BERT](https://mlsanity.com/paper/2405.15039/) (2024)
- [FREE: Fast and Robust Vision Language Models with Early Exits](https://mlsanity.com/paper/2506.06884/) (2025)
- [BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts](https://mlsanity.com/paper/2502.00745/) (2025)
- [Distributed Inference on Mobile Edge and Cloud: An Early Exit based Clustering Approach](https://mlsanity.com/paper/2410.05338/) (2024)
- [Beyond Greedy Exits: Improved Early Exit Decisions for Risk Control and Reliability](https://mlsanity.com/paper/2509.23666/) (2025)
- [Distributed Inference on Mobile Edge and Cloud: A Data-Cartography based Clustering Approach](https://mlsanity.com/paper/2412.16616/) (2024)
- [Know What You Don't Know: Selective Prediction for Early Exit DNNs](https://mlsanity.com/paper/2509.11520/) (2025)
- [Hetero-SplitEE: Split Learning of Neural Networks with Early Exits for Heterogeneous IoT Devices](https://mlsanity.com/paper/2512.09313/) (2025)
- [SPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference](https://mlsanity.com/paper/2608.13076/) (2026)

## Abstract (arXiv)

The recent advances in Deep Neural Networks (DNNs) stem from their exceptional performance across various domains. However, their inherent large size hinders deploying these networks on resource-constrained devices like edge, mobile, and IoT platforms. Strategies have emerged, from partial cloud computation offloading (split computing) to integrating early exits within DNN layers. Our work presents an innovative unified approach merging early exits and split computing. We determine the 'splitting layer', the optimal depth in the DNN for edge device computations, and whether to infer on edge device or be offloaded to the cloud for inference considering accuracy, computational efficiency, and communication costs. Also, Image classification faces diverse environmental distortions, influenced by factors like time of day, lighting, and weather. To adapt to these distortions, we introduce I-SplitEE, an online unsupervised algorithm ideal for scenarios lacking ground truths and with sequential data. Experimental validation using Caltech-256 and Cifar-10 datasets subjected to varied distortions showcases I-SplitEE's ability to reduce costs by a minimum of 55% with marginal performance degradation of at most 5%.