We are pleased to announce the publication of our latest conference paper, titled “Learning-Enabled CPS for Edge-Cloud Computing”, in the proceedings of the 2024 IEEE International Symposium on Industrial Embedded Systems (SIES).
Abstract
Many Cyber-Physical Systems (CPS), such as autonomous vehicles and robots, rely on compute-intensive Machine Learning (ML) algorithms, especially for perception processing. A growing trend is to implement such ML algorithms in the cloud. However, the data transfer overhead and the delay introduced in the process necessitate some form of edge-cloud solution. Here, a part of the processing is done locally and the rest on the cloud, and how to do this partitioning is being explored in the body of work referred to as Split Computing (SC). In this position paper, we explore different SC architectures and discuss their implications on controller design for CPS. In particular, we discuss the delay and state estimation accuracy of these different SC architectures and how they would impact the design of the feedback controllers using them.
Details
- Title: Learning-Enabled CPS for Edge-Cloud Computing
- Authors: Luigi Capogrosso, Shengjie Xu, Enrico Fraccaroli, Marco Cristani, Franco Fummi, Samarjit Chakraborty
- Conference: IEEE 14th International Symposium on Industrial Embedded Systems (SIES)
- Year: 2024
- Pages: 132-139
- Keywords: Machine learning algorithms, Embedded systems, Computer architecture, Machine learning, Market research, Data transfer, Delays, Partitioning algorithms, State estimation, Robots, Split Computing, Early Exit, Deep Neural Networks, Cyber-Physical Systems, Edge Devices
Links
- DOI: 10.1109/SIES62473.2024.10767956
- Open Access Version: Read Here
This position paper explores novel Split Computing (SC) architectures to address the challenges posed by cloud-based Machine Learning (ML) algorithms in Cyber-Physical Systems (CPS). By examining delay, state estimation accuracy, and their implications on feedback controller design, the paper provides valuable insights for enhancing CPS performance.
We extend our gratitude to all collaborators and contributors for their efforts. For more details, please explore the links provided above.