Tag: model inference

New Methodology for Efficient Design Space Exploration in Next-Gen Cyber-Physical Systems

Posted on by bennyakesson

I’m excited to share that our journal article “CompDSE: A Methodology for Design Space Exploration of Computing Subsystems within Complex Cyber-Physical Systems” has been accepted for publication in IET Cyber-Physical Systems: Theory and Applications! The article, first-authored by Faezeh Sadat Saadatmand, outlines the model-based Design Space Exploration (DSE) approach we used in the DSE2.0 project. This project is a collaboration between Leiden University, the University of Amsterdam, and ASML, and is co-funded by NWO and TNO-ESI.

Our work addresses the need for efficient DSE techniques to evaluate potential design decisions and their impact on non-functional aspects like performance, reliability, and energy consumption in next-gen complex distributed cyber-physical systems (dCPS).

In the article, we introduce CompDSE, a methodology designed to facilitate the DSE of complex dCPS, with a focus on the computing subsystems. CompDSE uses abstract models of the application workload, computing hardware platform, and workload-to-platform mapping, all automatically derived from runtime trace data. These models are integrated into a discrete event simulation environment to explore various design points.

We demonstrate the effectiveness of our methodology through a case study on the ASML Twinscan lithography machine, a complex industrial dCPS. The results show potential performance enhancements by optimizing computing subsystems while considering physical constraints. Each design point evaluation takes less than a minute, highlighting CompDSE’s efficiency and scalability in tackling complex dCPS with large design spaces.

Automatic Workload Inference Improves Scalability of DSE in Complex Systems

Posted on by bennyakesson

I am happy to announce that the paper “Automated Derivation of Application Workload Models for Design Space Exploration of Industrial Distributed Cyber-Physical Systems” has been accepted for publication at the 7th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS). The paper is first-authored by Faezeh Saadatmand in the context of the DSE2.0 project, a part of the academic research program MasCot, co-funded by TNO-ESI and NWO. Congratulations Faezeh!

The paper addresses challenges with respect to designing their next-generation distributed cyber-physical systems (dCPS). Efficient Design Space Exploration (DSE) techniques are needed to evaluate possible design decisions and their consequences on non-functional aspects of the systems. To enable scalable and efficient DSE of complex dCPS, it is essential to have abstract and coarse-grained models that are both accurate and capable of capturing dynamic application workloads. However, manually creating such models is time-consuming and error-prone, and they need to be continuously updated as the system evolves. This research addresses this need by introducing an automatic method for deriving an application workload model. This model, based on trace analysis, captures computation and communication activities within an application in a timing-agnostic manner. The approach has been validated through a case study on an ASML Twinscan lithography machine, demonstrating high accuracy in capturing real application workloads. Next steps in this research involves combining this model with an automatically inferred hardware platform model to enable DSE exploring different hardware, software, and mapping alternatives.