Projects

My group conducts research in the area of machine learning and quantum computing with a focus on hardware/algorithm co-design, which covers very large-scale integrated circuit (VLSI) design, computer architecture, electronic design automation, and machine learning/quantum computing algorithms et al. A number of on-going research is listed below.

1) Machine learning for science and engineering, e.g., electronic design automation. We are now exploring machine learning methods to automate the design of circuits and computing systems. For example, we leverage reinforcement learning to automatically size analog/radio-frequency (RF) circuits and use graph learning to generate analog circuit topologies. Please check the prior publications [DAC '22][DAC '23][ICLR '23] for details.

2) Efficient hardware accelerator for machine learning applications. We explore the co-optimization of devices, efficient VLSI, emerging computing architectures (e.g., in- and near-sensor computing and in- and near-memory computing), and algorithms to unleash the performance of machine learning on hardware. Please check my prior publications [DATE '19][ICCAD '19][TCAD '20][TCAD '21][TC '22][ISLPED '22][ISCA '23][ISCAS '23] for details.

3) Robust and scalable quantum computing. We also do fundamental research to enhance the robustness and scalability of superconducting quantum computing systems through the design of circuits and algorithms (e.g., Ising Machine and Topological Electronics). Please check the prior publications [Nature Nanotechnology '22][Scientific Reports '22] for details.