# ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence

arXiv: https://arxiv.org/abs/2203.13061 · HTML: https://mlsanity.com/paper/2203.13061/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud](https://mlsanity.com/paper/2008.06402/) (2020)
- [Learning to Simulate Dynamic Environments with GameGAN](https://mlsanity.com/paper/2005.12126/) (2020)
- [JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1801.08618/) (2018)
- [Deep feature compression for collaborative object detection](https://mlsanity.com/paper/1802.03931/) (2018)
- [HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units](https://mlsanity.com/paper/2008.04063/) (2020)
- [Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools](https://mlsanity.com/paper/1903.11314/) (2019)
- [EdgeRec: Recommender System on Edge in Mobile Taobao](https://mlsanity.com/paper/2005.08416/) (2020)
- [Pareto-Optimal Bit Allocation for Collaborative Intelligence](https://mlsanity.com/paper/2009.12430/) (2020)
- [The server is dead, long live the server: Rise of Serverless Computing, Overview of Current State and Future Trends in Research and Industry](https://mlsanity.com/paper/1906.02888/) (2019)
- [SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning](https://mlsanity.com/paper/2001.01043/) (2020)
- [Dependability in Edge Computing](https://mlsanity.com/paper/1710.11222/) (2017)
- [ShadowTutor: Distributed Partial Distillation for Mobile Video DNN Inference](https://mlsanity.com/paper/2003.10735/) (2020)
- [ElfStore: A Resilient Data Storage Service for Federated Edge and Fog Resources](https://mlsanity.com/paper/1905.08932/) (2019)

## Abstract (arXiv)

Intelligent applications based on machine learning are impacting many parts of our lives. They are required to operate under rigorous practical constraints in terms of service latency, network bandwidth overheads, and also privacy. Yet current implementations running in the Cloud are unable to satisfy all these constraints. The Edge-Cloud Collaborative Intelligence (ECCI) paradigm has become a popular approach to address such issues, and rapidly increasing applications are developed and deployed. However, these prototypical implementations are developer-dependent and scenario-specific without generality, which cannot be efficiently applied in large-scale or to general ECC scenarios in practice, due to the lack of supports for infrastructure management, edge-cloud collaborative service, complex intelligence workload, and efficient performance optimization. In this article, we systematically design and construct the first unified platform, ACE, that handles ever-increasing edge and cloud resources, user-transparent services, and proliferating intelligence workloads with increasing scale and complexity, to facilitate cost-efficient and high-performing ECCI application development and deployment. For verification, we explicitly present the construction process of an ACE-based intelligent video query application, and demonstrate how to achieve customizable performance optimization efficiently. Based on our initial experience, we discuss both the limitations and vision of ACE to shed light on promising issues to elaborate in the approaching ECCI ecosystem.