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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.

Recent trends in the automotive industry show growing demands for the introduction of new in-vehicle features (e.g., smart-phone integration, adaptive cruise control, etc.) at increasing rates and with reduced time-to-market. New technological developments (e.g., in-vehicle Ethernet, multi-core technologies, AUTOSAR standardized software architectures, smart video and radar sensors, etc.) provide opportunities as well as challenges to automotive designers for introducing and implementing new features at lower costs, and with increased safety and security. As a result, the design of Electrical/Electronic (E/E) architectures is becoming increasingly challenging as several hardware resources are needed. In our earlier work, we have provided top-level definitions for three relevant metrics that can be used to evaluate E/E architecture alternatives in the early stages of the design process: flexibility, scalability and expandability.

Current development processes for automotive Electronic Control System (ECS) architectures have certain limitations in evaluating and comparing different architecture design alternatives. The limitations entail the lack of systematic and quantitative exploration and evaluation approaches that enable objective comparison of architectures in the early phases of the design cycle. In addition, architecture design is a multi-stage process, and entails several stakeholders who typically use their own metrics to evaluate different architecture design alternatives. Hence, there is no comprehensive view of which metrics should be used, and how they should be defined. Finally, there are often conflicting forces pulling the architecture design toward short-term objectives such as immediate cost savings versus more flexible, scalable or reliable solutions. In this paper, we propose the usage of a set of metrics for comparing ECS architecture alternatives.

Emerging technologies provide opportunities for the implementation of advanced car features enhancing the safety and the comfort of the driver, but at the same time, the correct implementation of these features imposes new design challenges on electronics, software, and controls designers due to the large number of in-vehicle computers and serial data communications. In this paper, we propose a comprehensive view of methods and tools that support the designers in facing such challenges. We propose an approach for quantitative architecture exploration based on the scoring of the possible alternatives via metrics of interest, and we illustrate some early results with a case study example.

With the increasing number of ECUs in modern automotive applications (70 in high end cars), designers are facing the challenge of managing complex design tasks, such as the allocation of software tasks over a network of ECUs. The allocation may be dictated by different attributes (performance, cost, size, etc.). The task of validating a given allocation can be achieved either via static analysis (e.g., for cost, size) and/or dynamic analysis (e.g. via performance simulation - for timing constraints). This paper brings together two key concepts: algorithmic and optimization techniques to be used during static analysis and virtual integration platforms for simulation-based exploration. The two concepts together provide the pillars for a constraint-driven / simulation-based approach, tailored to optimize the entire ECU network according to a cost function defined by the user.

Modern automotive applications such as X-by-Wire are implemented over distributed architectures where electronic control units (ECU's) communicate via broadcast buses. In this paper, we present a framework for quick exploration of design alternatives in terms of HW/SW architectures for distributed applications. The exploration is carried out on a virtual integration platform that allows the distribution of embedded software onto ECU's. The framework shortens design turn-around time by supporting semi-automatic communication protocol model configuration (e.g. frame packaging, redundancy level, etc.), and then by allowing the designer to run fast yet accurate simulations of a virtual prototype of the distributed architecture that includes models of the application software and the bus communication protocols.