Scientific publications

Multi-scale dataflow models have actors acting at multiple granularity levels, e.g., a dataflow model of a video processing application with operations on frame, line, and pixel level. The state of the art timing analysis methods for both static and dynamic dataflow types aggregate the behaviours across all granularity levels into one, often large iteration, which is repeated without exploiting the structure within such an iteration.

Memory-bound applications heavily depend on the bandwidth of the system in order to achieve high performance. Improving temporal and/or spatial locality through loop transformations is a common way of mitigating this dependency. However, choosing the right combination of optimizations is not a trivial task, due to the fact that most of them alter the memory access pattern of the application and as a result interfere with the efficiency of the hardware prefetching mechanisms present in modern architectures.

Abstraction, in the context of model checking, is aimed at reducing the state space of the system by omitting details that are irrelevant to the property being verified. Many successful approaches to the "state explosion problem," some of them described in other chapters, can be seen as abstractions. In this chapter, several notions of abstraction are considered in a uniform setting.

The booming popularity of data science is also affecting high-tech industries. However, since these usually have different core competencies — building cyber-physical systems rather than e.g. machine learning or data mining algorithms — delving into data science by domain experts such as system engineers or architects might be more cumbersome than expected.

The complexity of cyber-physical systems is increasing, driven by integration of more functionality and trends towards mass-customization. This has resulted in complex systems with many variants that require long time to develop and are difficult to adapt to changing requirements and introduction of new technology.

Over the last decades, the complexity of high-tech systems, and the softwaresystems controlling them,has increased considerably. In practice, it is hard to keep knowledge and documentation of these ever-evolving software systems up-to-date with their actual realization; we are dealing with legacy software.

Many high data-rate video-processing applications are subject to a trade-off between throughput and the sizes of buffers in the system (the storage distribution). These applications have strict requirements with respect to throughput as this directly relates to the functional correctness. Furthermore, the size of the storage distribution relates to resource usage which should be minimized in many practical cases.

Complex real-time video processing applications with strict throughput constraints are commonly found in a typical healthcare application. The video processing chain is implemented as Field-Programmable Gate Array (FPGA) accelerators (processing blocks) communicating through a number of First-In First-Out (FIFO) buffers.