Efficient Transistor Sizing Optimization through Knowledge Alignment and Transfer across Designs and Technologies

KATO, a novel Bayesian optimization framework, enables efficient transistor sizing by transferring knowledge across different circuit designs and technology nodes, delivering up to 2x simulation reduction and 1.2x design improvement over state-of-the-art methods.