Real-time Mode-Aware Dataflow: A Dataflow Model to Specify and Analyze Mode-dependent CPSs under Relaxed Timing Constraints
Modern Cyber-Physical Systems (CPS) often exhibit both relaxed real-time constraints and a mode-dependent execution. Relaxed real-time constraints mean that only a subset of the processes of a CPS have real-time constraints, and a mode-dependent CPS has conditional execution branches. Static analysis tools, such as the PolyGraph model (a formalism extending the Cyclo-Static Dataflow model with real-time constraints), can specify and analyze systems with relaxed real-time constraints. However, PolyGraph is limited in its ability to specify and analyze mode-dependent CPSs. This paper extends PolyGraph with routing actors, yielding the Routed PolyGraph model. This model is further extended to the Real-time Mode-Aware Dataflow (RMDF), which both leverages routing actors and incorporates a new dataflow actor to specify mode-dependent CPSs under relaxed real-time constraints. This paper also extends the static analyses of PolyGraph to RMDF. We showcase the application of RMDF with a specification and an analysis (derivation of timing constraints at the job-level and a feasibility test) of the vision processing system of the Ingenuity Mars helicopter.
Code (0)
등록된 구현이 없습니다.
Tasks
IngenuitySimilar Papers 제목 키워드 기반
The Ingenuity Mars Helicopter Specified and Analyzed with the Real-time Mode-aware Dataflow Model
Ingenuity is an autonomous Cyber-Pysical System (CPS) that has successfully completed more than 70 flights over Mars between 2021 and 2024. Ensuring the safety of its mission is paramount, as any failure could result in …
IngenuityFlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System
Dataflow visualization systems enable flexible visual data exploration by allowing the user to construct a dataflow diagram that composes query and visualization modules to specify system functionality. However learning …
Machine TranslationHASS: Hardware-Aware Sparsity Search for Dataflow DNN Accelerator
Deep Neural Networks (DNNs) excel in learning hierarchical representations from raw data, such as images, audio, and text. To compute these DNN models with high performance and energy efficiency, these models are usually…
A Data-Driven Approach to Dataflow-Aware Online Scheduling for Graph Neural Network Inference
Graph Neural Networks (GNNs) have shown significant promise in various domains, such as recommendation systems, bioinformatics, and network analysis. However, the irregularity of graph data poses unique challenges for ef…
CPUGPUGraph Neural NetworkRecommendation Systems+1Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation
Distributed dataflow systems like Spark and Flink enable the use of clusters for scalable data analytics. While runtime prediction models can be used to initially select appropriate cluster resources given target runtime…
Descriptive