Aug 7 (Mon) @ 9:00am: "Uncertainty-Aware and Efficient Design Automation from Classical Circuit to Quantum Computing," Zichang He, ECE PhD Defense
In the twilight of Moore's Law, the semiconductor industry grapples with escalating challenges driven by growing process variations and inherent uncertainties in the manufacturing of increasingly miniaturized devices. Traditional electronic design automation (EDA) tools attempt to address this through rigorous simulation, modeling, and optimization. Concurrently, the need for alternative computational paradigms, notably quantum computing, has surged, introducing fresh complexities and challenges. This dissertation primarily addresses the high cost and low efficiency of modeling and simulation, and the difficulties in design optimization under conditions of noisy and expensive simulations in the context of classical circuits and quantum computing. It proposes uncertainty-aware and efficient design automation methodologies in the EDA field and quantum computing. The first part focuses on the circuit-level uncertainty quantification and optimization of classical circuits. Meanwhile, the second part addresses block-level simulation and optimization of quantum algorithms.
Zichang He is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara, advised by Professor Zheng Zhang. Zichang's research activities mainly focus on the intersections among design automation, machine learning, and quantum computing. He is the recipient of two best student paper awards in IEEE Electrical Performance of Electronic Packaging and Systems (EPEPS) 2020 and IEEE High Performance Extreme Computing (HPEC) 2022, and IEE Excellent in Research Fellowship at UCSB in 2021.
Hosted by: Professor Zheng Zhang
Submitted by: Zichang He <firstname.lastname@example.org>