Prof. Song Han

Professor Han is interested in application-driven, domain-specific computer architecture research. The end of Dennard scaling makes power become the key constraint. He is interested in achieving higher efficiency by tailoring the architecture to characteristics of the application domain. Current research center around co-designing efficient algorithms and hardware systems for machine learning, to free AI from the power hungry hardware beasts and democratize AI to cheap mobile devices, and also reduce the cost of running deep learning on data centers. Han enjoys the research intersections across machine learning algorithms, computer architecture and VLSI design.