# Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms

arXiv: https://arxiv.org/abs/1802.03835 · HTML: https://mlsanity.com/paper/1802.03835/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2018.

## Built on by (most cited first, 30 of 35)

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- [Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency](https://mlsanity.com/paper/2311.05739/) (2023)
- [Edge-Cloud Collaborated Object Detection via Difficult-Case Discriminator](https://mlsanity.com/paper/2108.12858/) (2021)
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- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers](https://mlsanity.com/paper/2107.10404/) (2021)
- [A Survey on Deep Neural Network Partition over Cloud, Edge and End Devices](https://mlsanity.com/paper/2304.10020/) (2023)
- [Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence](https://mlsanity.com/paper/2312.16497/) (2023)
- [Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning](https://mlsanity.com/paper/2207.11019/) (2022)
- [Robust and Scalable Hyperdimensional Computing With Brain-Like Neural Adaptations](https://mlsanity.com/paper/2311.07705/) (2023)
- [Communication Efficient Split Learning of ViTs with Attention-based Double Compression](https://mlsanity.com/paper/2509.15058/) (2025)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [opp/ai: Optimistic Privacy-Preserving AI on Blockchain](https://mlsanity.com/paper/2402.15006/) (2024)
- [ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer](https://mlsanity.com/paper/2105.02019/) (2021)
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- [An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic](https://mlsanity.com/paper/2201.02968/) (2022)
- [Facial Data Minimization: Shallow Model as Your Privacy Filter](https://mlsanity.com/paper/2310.15590/) (2023)

## Abstract (arXiv)

This paper introduces partitioning an inference task of a deep neural network between an edge and a host platform in the IoT environment. We present a DNN as an encoding pipeline, and propose to transmit the output feature space of an intermediate layer to the host. The lossless or lossy encoding of the feature space is proposed to enhance the maximum input rate supported by the edge platform and/or reduce the energy of the edge platform. Simulation results show that partitioning a DNN at the end of convolutional (feature extraction) layers coupled with feature space encoding enables significant improvement in the energy-efficiency and throughput over the baseline configurations that perform the entire inference at the edge or at the host.