# JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services

arXiv: https://arxiv.org/abs/1801.08618 · HTML: https://mlsanity.com/paper/1801.08618/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

## Built on by (most cited first, 30 of 80)

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- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
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- [Supervised Compression for Resource-Constrained Edge Computing Systems](https://mlsanity.com/paper/2108.11898/) (2021)
- [BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing](https://mlsanity.com/paper/2201.02693/) (2022)
- [A Survey on Collaborative DNN Inference for Edge Intelligence](https://mlsanity.com/paper/2207.07812/) (2022)
- [Privacy-preserving Adversarial Facial Features](https://mlsanity.com/paper/2305.05391/) (2023)
- [Collaborative Intelligence: Challenges and Opportunities](https://mlsanity.com/paper/2102.06841/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AI](https://mlsanity.com/paper/2312.14229/) (2023)
- [SC2 Benchmark: Supervised Compression for Split Computing](https://mlsanity.com/paper/2203.08875/) (2022)
- [SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems](https://mlsanity.com/paper/2204.04705/) (2022)
- [Pipeline Parallelism for Inference on Heterogeneous Edge Computing](https://mlsanity.com/paper/2110.14895/) (2021)
- [Decentralized Low-Latency Collaborative Inference via Ensembles on the Edge](https://mlsanity.com/paper/2206.03165/) (2022)
- [Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning](https://mlsanity.com/paper/2102.02638/) (2021)
- [Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey](https://mlsanity.com/paper/2309.15467/) (2023)
- [Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence](https://mlsanity.com/paper/2105.07102/) (2021)
- [I-SPLIT: Deep Network Interpretability for Split Computing](https://mlsanity.com/paper/2209.11607/) (2022)
- [Accelerating End-Cloud Collaborative Inference via Near Bubble-free Pipeline Optimization](https://mlsanity.com/paper/2501.12388/) (2024)
- [Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computation](https://mlsanity.com/paper/2105.00591/) (2021)
- [On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems](https://mlsanity.com/paper/2201.10382/) (2022)
- [Privacy-Preserving Visual Localization with Event Cameras](https://mlsanity.com/paper/2212.03177/) (2022)
- [CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative Intelligence](https://mlsanity.com/paper/2106.05531/) (2021)
- [Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks](https://mlsanity.com/paper/2202.10541/) (2022)
- [T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit](https://mlsanity.com/paper/2207.06613/) (2022)
- [Hierarchical Training of Deep Neural Networks Using Early Exiting](https://mlsanity.com/paper/2303.02384/) (2023)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Fingerprinting Multi-exit Deep Neural Network Models via Inference Time](https://mlsanity.com/paper/2110.03175/) (2021)
- [Distributed Semantic Segmentation with Efficient Joint Source and Task Decoding](https://mlsanity.com/paper/2407.11224/) (2024)

## Abstract (arXiv)

Deep learning models are being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants, autonomous cars, and smart home services often employ either simple local models on the mobile or complex remote models on the cloud. However, recent studies have shown that partitioning the DNN computations between the mobile and cloud can increase the latency and energy efficiencies. In this paper, we propose an efficient, adaptive, and practical engine, JointDNN, for collaborative computation between a mobile device and cloud for DNNs in both inference and training phase. JointDNN not only provides an energy and performance efficient method of querying DNNs for the mobile side but also benefits the cloud server by reducing the amount of its workload and communications compared to the cloud-only approach. Given the DNN architecture, we investigate the efficiency of processing some layers on the mobile device and some layers on the cloud server. We provide optimization formulations at layer granularity for forward- and backward-propagations in DNNs, which can adapt to mobile battery limitations and cloud server load constraints and quality of service. JointDNN achieves up to 18 and 32 times reductions on the latency and mobile energy consumption of querying DNNs compared to the status-quo approaches, respectively.