Hardware Simulation

Hardware simulation aims to accurately and efficiently model the behavior of electronic circuits and systems, enabling faster design exploration and optimization before physical fabrication. Current research emphasizes developing faster simulation algorithms, such as those leveraging quadratic programming and machine learning techniques (e.g., convolutional neural networks, graph attention networks), to overcome the computational bottlenecks associated with increasingly complex designs, particularly in areas like analog computing and AI accelerators. These advancements are crucial for accelerating the development of energy-efficient computing architectures and improving the design process for integrated circuits, impacting both academic research and industrial applications.