Modeling of End-to-End Resource Reservations in Component-Based Vehicular Embedded Systems

2016 ◽  
Author(s):  
Saad Mubeen ◽  
Mohammad Ashjaei ◽  
Thomas Nolte ◽  
John Lundback ◽  
Mattias Galnander ◽  
...  
Keyword(s):  
Embedded Systems ◽  
End To End ◽  
Resource Reservations

scholarly journals Designing end-to-end resource reservations in predictable distributed embedded systems

2017 ◽  
Vol 53 (6) ◽  
pp. 916-956 ◽  
Author(s):  
Mohammad Ashjaei ◽  
Nima Khalilzad ◽  
Saad Mubeen ◽  
Moris Behnam ◽  
Ingo Sander ◽  
...  
Keyword(s):  
Embedded Systems ◽  
Distributed Embedded Systems ◽  
End To End ◽  
Resource Reservations

scholarly journals Design Methods and Processes for ML/DL models

2021 ◽  
Author(s):  
◽  
Meenu Mary John
Keyword(s):  
Embedded Systems ◽  
Design Methods ◽  
Key Factors ◽  
High Performing ◽  
Structured Design ◽  
Continuous Delivery ◽  
End To End ◽  
Access To Data ◽  
Structured Design Methods ◽  
Decentralized Architecture

Context: With the advent of Machine Learning (ML) and especially Deep Learning (DL) technology, companies are increasingly using Artificial Intelligence (AI) in systems, along with electronics and software. Nevertheless, the end-to-end process of developing, deploying and evolving ML and DL models in companies brings some challenges related to the design and scaling of these models. For example, access to and availability of data is often challenging, and activities such as collecting, cleaning, preprocessing, and storing data, as well as training, deploying and monitoring the model(s) are complex. Regardless of the level of expertise and/or access to data scientists, companies in all embedded systems domain struggle to build high-performing models due to a lack of established and systematic design methods and processes. Objective: The overall objective is to establish systematic and structured design methods and processes for the end-to-end process of developing, deploying and successfully evolving ML/DL models. Method: To achieve the objective, we conducted our research in close collaboration with companies in the embedded systems domain using different empirical research methods such as case study, action research and literature review. Results and Conclusions: This research provides six main results: First, it identifies the activities that companies undertake in parallel to develop, deploy and evolve ML/DL models, and the challenges associated with them. Second, it presents a conceptual framework for the continuous delivery of ML/DL models to accelerate AI-driven business in companies. Third, it presents a framework based on current literature to accelerate the end-to-end deployment process and advance knowledge on how to integrate, deploy and operationalize ML/DL models. Fourth, it develops a generic framework with five architectural alternatives for deploying ML/DL models at the edge. These architectural alternatives range from a centralized architecture that prioritizes (re)training in the cloud to a decentralized architecture that prioritizes (re)training at the edge. Fifth, it identifies key factors to help companies decide which architecture to choose for deploying ML/DL models. Finally, it explores how MLOps, as a practice that brings together data scientist teams and operations, ensures the continuous delivery and evolution of models.

scholarly journals Verified Lustre Normalization with Node Subsampling

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Timothy Bourke ◽  
Paul Jeanmaire ◽  
Basile Pesin ◽  
Marc Pouzet
Keyword(s):  
Embedded Systems ◽  
Code Generation ◽  
Reactive Systems ◽  
Type System ◽  
Theorem Prover ◽  
Correctness Proof ◽  
Assembly Code ◽  
Function Calls ◽  
End To End ◽  
Activation Conditions

Dataflow languages allow the specification of reactive systems by mutually recursive stream equations, functions, and boolean activation conditions called clocks. Lustre and Scade are dataflow languages for programming embedded systems. Dataflow programs are compiled by a succession of passes. This article focuses on the normalization pass which rewrites programs into the simpler form required for code generation. Vélus is a compiler from a normalized form of Lustre to CompCert’s Clight language. Its specification in the Coq interactive theorem prover includes an end-to-end correctness proof that the values prescribed by the dataflow semantics of source programs are produced by executions of generated assembly code. We describe how to extend Vélus with a normalization pass and to allow subsampled node inputs and outputs. We propose semantic definitions for the unrestricted language, divide normalization into three steps to facilitate proofs, adapt the clock type system to handle richer node definitions, and extend the end-to-end correctness theorem to incorporate the new features. The proofs require reasoning about the relation between static clock annotations and the presence and absence of values in the dynamic semantics. The generalization of node inputs requires adding a compiler pass to ensure the initialization of variables passed in function calls.

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