Embedded systems are computer systems that perform dedicated functions while being parts of a larger system. Our research targets primarily heterogeneous many-core System on a Chip (SoC) platforms where communication happens via the network-on-chip. These SoCs should be designed to meet aggressive performance requirements, while coping with limited battery capacity, thermal design power, and real-time constraints. Over the years, we have considered deterministic, probabilistic, and statistical physics-inspired design paradigms. Lately, our research targets machine learning approaches (e.g., imitation and reinforcement learning) for performance and energy optimization and resource management in heterogeneous SoC platforms.
Cyber-physical systems (CPS) refer to a new generation of networked embedded systems that bring together sensing, computation, communication, control and actuation in order to sustain a continuous interaction with the physical world (e.g., processes taking place on electrical power grids, transportation and traffic roads, communication and financial networks, medical devices, smart buildings, etc.). Physical processes are predominantly non-stationary in nature and require time-dependent models for understanding their behavior. Our research focuses on accurate modeling physical processes to better understand the theoretical foundations of CPS design and optimization.
Prof. Radu Marculescu
System Level Design Group
Electrical and Computer Engineering
The University of Texas at Austin
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