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To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

Automotive HW/SW architectures are becoming increasingly complex to support the deployment of new safety, comfort, and energy-efficiency features. Such architectures include several software tasks (100+), messages (1000+), computational and communication resources (70+ CPUs, 10+ buses), and (smart) sensors and actuators (20+). To cope with the increasing system complexity at lowest development and product costs, highest safety, and fastest time to market, model-based rapid-prototyping development processes are essential. The processes, coupled with optimization steps aimed at reducing the number of software and hardware resources while satisfying the safety requirements, enable reduction of the system complexity and ease downstream testing/validation efforts. This paper describes a novel model-based design exploration and optimization process for the deployment of a set of software tasks on a dual-processor control module implementing a fail-safe strategy.

Multi-core systems are promising a cost-effective solution for (1) advanced vehicle features requiring dramatically more software and hence an order of magnitude more processing power, (2) redundancy and mixed-IP, mixed-ASIL isolation required for ISO 26262 functional safety, and (3) integration of previously separate ECUs and evolving embedded software business models requiring separation of different software parts. In this context, designing, optimizing and verifying the mapping and scheduling of software functions onto multiple processing cores becomes key. This paper describes several multi-core task design and scheduling design options, including function-to-task mapping, task-to-core allocation (both static and dynamic), and associated scheduling policies such as rate-monotonic, criticality-aware priority assignment, period transformation, hierarchical partition scheduling, and dynamic global scheduling.

Systems and network integration is a major challenge, and systematic analysis of the complex dynamic timing effects becomes key to building reliable systems. Very often, however, systematic analysis techniques are (considered) too restrictive with respect to established design practice. In this paper we present lessons learned from real-world case studies, in which OEMs have used the new SymTA/S scheduling analysis technology to evaluate different network choices with minimum effort. Thanks to its flexibility and supplier independence, SymTA/S can be applied in non-ideal situations, where other, more restricted technologies are inherently limited. Finally, we put the technology into relation with ongoing standardization activities.

Today, gated networks with several buses are becoming standard in automotive E/E-systems but are evolving differently among the various vehicle manufactures, with different topologies, combinations of bus protocols, and speeds. Making the right architecture decisions requires systematic evaluation of the many alternatives during early design stages. However, there are many trade-offs in terms of performance, cost, extensibility, etc.. In this context, scheduling analysis is a powerful tool. It clarifies performance, end-to-end timing, and dynamic behavior. This enables evaluation of networking alternatives, foresight of bottlenecks, and provides guidance in the design process. In the paper, the application of scheduling analysis in automotive network exploration and optimization will be demonstrated. Specific emphasize will be put on end-to-end timing, migration from CAN to FlexRay, black-box integration and early-stage assumptions, extensibility, and trade-offs.

When designing safety-critical automotive systems, verification of timing and performance are key, especially the verification of hard deadlines and other critical timing constraints. Test- or simulation-based approaches suffer from corner-case coverage problems and are becoming less reliable as systems grow in size and complexity. Time-triggered mechanisms (e.g. OSEKtime and FlexRay) were proposed as a way out by providing better timing prediction. However, for reasons of cost, flexibility and reactivity, future cars will mostly likely contain a mix of event-triggered (ET) and time-triggered (TT) components that are combined synchronously and/or asynchronously, thereby further complicating timing. Scheduling analysis has recently matured to allow reliable timing verification and systematic optimization for ET, TT, and mixed systems.