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A cyber-physical system (CPS) is an integration of computation with physical processes whose behavior is defined by both computational and physical parts of the system. In this paper, we present a view of the challenges and opportunities for design automation of CPS. We identify a combination of characteristics that define the challenges unique to the design automation of CPS. We then present selected promising advances in depth, focusing on four foundational directions: combining model-based and data-driven design methods; design for humanin-the-loop systems; component-based design with contracts, and design for security and privacy. These directions are illustrated with examples from two application domains: smart energy systems and next-generation automotive systems.

SAT sweeping is a method for simplifying an AND/INVERTER graph (AIG) by systematically merging graph vertices from the inputs towards the outputs using a combination of structural hashing, simulation, and SAT queries. Due to its robustness and efficiency, SAT sweeping provides a solid algorithm for Boolean reasoning in functional verification and logic synthesis. In previous work, SAT sweeping merges two vertices only if they are functionally equivalent. In this paper we present a significant extension of the SAT-sweeping algorithm that exploits local observability don'tcares (ODCs) to increase the number of vertices merged. We use a novel technique to bound the use of ODCs and thus the computational effort to find them, while still finding a large fraction of them. Our reported results based on a set of industrial benchmark circuits demonstrate that ODC-based SAT sweeping results in significantly more graph simplification with great benefit for Boolean reasoning with a moderate increase in computational effort.

Abstract-Buildings account for nearly 40 percent of the total energy consumption in the United States. As a critical step toward smart cities, it is essential to intelligently manage and coordinate the building operations to improve the efficiency and reliability of overall energy system. With the advent of smart meters and two-way communication systems, various energy consumptions from smart buildings can now be coordinated across the smart grid together with other energy loads and power plants. In this paper, we propose a comprehensive framework to integrate the operations of smart buildings into the energy scheduling of bulk power system through proactive building demand participation. This new scheme enables buildings to proactively express and communicate their energy consumption preferences to smart grid operators rather than passively receive and react to market signals and instructions such as time varying electricity prices. The proposed scheme is implemented in a simulation environment. The experiment results show that the proactive demand response scheme can achieve up to 10 percent system generation cost reduction and 20 percent building operation cost reduction compared with passive demand response scheme. The results also demonstrate that the system cost savings increase significantly with more flexible load installed and higher percentage of proactive customers participation level in the power network.

We consider a set of control tasks that must be executed on distributed platforms so that end-to-end latencies are within deadlines. We investigate how to allocate tasks to nodes, pack signals to messages, allocate messages to buses, and assign priorities to tasks and messages, so that the design is extensible and robust with respect to changes in task requirements. We adopt a notion of extensibility metric that measures how much the execution times of tasks can be increased without violating end-to-end deadlines. We optimize the task and message design with respect to this metric by adopting a mathematical programming front-end followed by postprocessing heuristics. The proposed algorithm as applied to industrial strength test cases shows its effectiveness in optimizing extensibility and a marked improvement in running time with respect to an approach based on randomized optimization.