BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems
Open accessAbstract
The emergence of various intelligent mobile applications demands the deployment of powerful deep learning models at resource-constrained mobile devices. The device-edge co-inference framework provides a promising solution by splitting a neural network at a mobile device and an edge computing server. In order to balance the on-device computation and the communication overhead, the splitting point needs to be carefully picked, while the intermediate feature needs to be compressed before transmission. Existing studies decoupled the design of model splitting, feature compression, and communication, which may lead to excessive resource consumption of the mobile device. In this paper, we introduce an end-to-end architecture, named BottleNet++, that consists of an encoder, a non-trainable channel layer, and a decoder for more efficient feature compression and transmission. The encoder and decoder essentially implement joint source-channel coding via lightweight convolutional neural networks (CNNs), while explicitly considering the effect of channel noise. By exploiting the strong sparsity and the fault-tolerant property of the intermediate feature in deep neural network (DNNs), BottleNet++ achieves a much higher compression ratio than existing methods. Compared with merely transmitting intermediate data without feature compression, BottleNet++ achieves up to 64× bandwidth reduction over the additive white Gaussian noise channel and up to 256× bit compression ratio in the binary erasure channel, with less than 2% reduction in accuracy of classification.
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In vertical edge inference, end-to-end training with joint source-channel coding reduces both communication overhead and on-device computation cost.
“It was first tested in vertical edge inference via end-to-end training with joint source-channel coding in [233], which helps to reduce both the communication overhead and on-device computation cost.”
From Edge artificial intelligence for 6g: vision, enabling technologies, and applications (2022) · cites this paper as [233]
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- Authors
- Shao, J., & Zhang, J.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Venue
- 2020 IEEE International Conference on Communications Workshops (ICC Workshops)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1-6
- ISSN
- 2164-7038 (ISSN-L)
- OpenAlex
- W2982333655
- Cited by
- 200
- References
- 25
- Citations by year
- 2026: 17 · 2025: 37 · 2024: 43 · 2023: 33 · 2022: 27 · 2021: 33 · 2020: 10
- FWCI
- 21.60
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Communications workshops (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Keywords
- feature compression, joint source-channel coding, model splitting, end-to-end architecture, encoder-decoder architecture, channel noise, bandwidth reduction, resource-constrained mobile devices, lightweight convolutional neural network, binary erasure channel, AWGN channel
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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