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Thu, May 20, 2021
Artificial Intelligence (A) ubiquitously impacts almost all research societies including electronic design automation (EDA). Many scholars with mathematic and modeling backgrounds have shifted their focuses onto applying AI technologies to their research or directly working on AI problems. As a researcher with a Ph.D. training of EDA and circuit designs, I started my AI-relevant research since late 2000s, i.e., neuromorphic computing that implements hardware to accelerate computation of biologically plausible learning models. In this talk, I will review the development process of my research from neuromorphic computing to a broader scope of AI, including machine learning accelerator designs, neural network quantization and pruning, neural architectural search, federated learning, and neural network robustness, privacy, security, etc., and how I benefit from my EDA background.