Scientific publications

One of the challenges in the design of supervisors with optimal throughput for manufacturing systems is the presence of behavior outside the control of the supervisor. Uncontrollable behavior is typically encountered in the presence of (user) inputs, external disturbances, and exceptional behavior. This paper introduces an approach for the modeling and synthesis of a throughput-optimal supervisor for manufacturing systems with partially-controllable behavior on two abstraction levels.

Cyber-physical systems (CPS) are complex, heterogeneous, and dynamic systems, spanning hardware and software components ranging from edge devices to cloud platforms. CPS need to satisfy many rigorous constraints, e.g., with respect to deadlines, safety, and quality, yielding a large configuration space where only a limited number of configurations meet the constraints and only a fraction are optimal regarding certain qualities.

The well-known model of Vestal aims to avoid excessive pessimism in the quantification of the processing requirements of mixed-criticality systems, while still guaranteeing the timeliness of higher-criticality functions. This can bring important savings in system costs, and indirectly help meet size, weight and power constraints.

Software interfaces play a crucial rule in component-based development. The Comma framework provides mature tool support for the specification and analysis of interfaces. An interface description in Comma contains a protocol state machine and it may specify timing constraints on the occurrence of interface events.

The processing of personal data is becoming a key business factor, especially for high-tech system industries such as automotive and healthcare service providers. To protect such data, the European Union (EU) has introduced the General Data Protection Regulation (GDPR), with the aim to standardize and strengthen data protection policies across EU countries.

The Halide DSL and compiler have enabled high-performance code generation for image processing pipelines targeting heterogeneous architectures through the separation of algorithmic description and optimization schedule. However, automatic schedule generation is currently only possible for multi-core CPU architectures.

Vehicles, as a prime example of high-tech systems, get increasingly connected and data-centric with the need to process personally identifiable information. Often, companies that develop such systems act as integrators and need to comply to adequate data protection requirements. For instance, GDPR requires securing personal data.

Monitoring and managing the health of technical systems with advanced diagnosis and prognosis benefits from fleet analytics: insights on the degradation of other but similar systems help, e.g., to forecast actual issues for predictive maintenance as does detecting and correcting anomalies in usage profiles helps to prevent undue wear and tear.

Tensor Cores (TCUs) are specialized units first introduced by NVIDIA in the Volta microarchitecture in order to accelerate matrix multiplications for deep learning and linear algebra workloads. While these units have proved to be capable of providing significant speedups for specific applications, their programmability remains difficult for the average user.

Automotive software implements different functionalities as multiple control applications sharing common platform resources. Although such applications are often developed independently, the control performance of the resulting system depends on how these applications are integrated. A key integration challenge is to efficiently schedule these applications on shared resources with minimal control performance degradation.