In the past decade, computer systems and chips have played a key role in the success of AI. Our vision in Google Brain's ML for Systems team is to use AI to transform the way in which computer systems and chips are designed. Many core problems in systems and hardware design are combinatorial optimization or decision making tasks on large graphs. In this talk, we will describe some of our latest learning based approaches to tackling such large-scale optimization problems and demonstrate how AI agents can automate and optimize complex tasks such as chip floorplanning in 6 hours, whereas existing baselines require human experts in the loop and take several weeks.
Azalia Mirhoseini is a Senior Research Scientist at Google Brain and an Advisor at Cmorq. She is the co-founder/lead of the Machine Learning for Systems Moonshot at Brain where they focus on deep reinforcement learning based approaches to solve problems in computer systems and metalearning... Read More →
Wednesday September 30, 2020 11:20am - 11:40am PDT
Virtual
