Generalized Deep Reinforcement Learning for Solving Combinatorial Optimization Problems


Many problems in systems and chip design are in the form of combinatorial optimization on graph structured data. In this talk, I will motivate taking a learning based approach to combinatorial optimization problems with a focus on deep reinforcement learning (RL) agents that generalize. I will discuss our work on a new domain-transferable reinforcement learning methodology for optimizing chip placement, a long pole in hardware design. Our approach is capable of learning from past experience and improving over time, resulting in more optimized placements on unseen chip blocks as the RL agent is exposed to a larger volume of data. Our objective is to minimize PPA (power, performance, and area), and we show that, in under 6 hours, our method can generate placements that are superhuman or comparable on modern accelerator chips, whereas existing baselines require human experts in the loop and can take several weeks.


Azalia Mirhoseini is a Senior Research Scientist at Google Brain. She is the co-founder/tech-lead of the Machine Learning for Systems Team in Google Brain where they focus on deep reinforcement learning based approaches to solve problems in computer systems and metalearning. She has a Ph.D. in Electrical and Computer Engineering from Rice University. She has received a number of awards, including the MIT Technology Review 35 under 35 award, the Best Ph.D. Thesis Award at Rice and a Gold Medal in the National Math Olympiad in Iran. Her work has been covered in various media outlets including MIT Technology Review, IEEE Spectrum, and Wired.