Dataflow Modeling for Reconfigurable Signal Processing Systems

International audienceNowadays, adaptive signal processing systems have become a reality. Their development has been mainly driven by the need of satisfying diverging constraints and changeable user needs, like resolution and throughput versus energy consumption. System runtime tuning, based on constraints/-conditions variations, can be effectively achieved by adopting reconfigurable computing infrastructures. These latter could be implemented either at the hardware or at the software level, but in any case their management and subsequent implementation is not trivial. In this chapter we present how dataflow models properties, as predictability and analyzability, can ease the development of reconfigurable signal processing systems, leading designers from modelling to physical system deployment. 1 Reconfigurable Signal Processing Systems For many years, the design of a signal processing system was mostly driven by performance requirements. Hence, design effort was mainly focused on optimizing the throughput and latency of the designed system, while satisfying constraints of reliability and quality of service, and minimizing the system production cost. In this context, a strong predictability of system behavior is essential, especially when designing safety-critical real-time systems. Many compile-time methodologies, computer-aided design tools, and static Model