Learning-Enabled CPS for Edge-Cloud Computing

Many Cyber-Physical System (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.