Reinforcement Learning for Placement Optimization

Abstract

In the past decade, computer systems and chips have played a key role in the success of artificial intelligence (AI). Our vision in Google Brain's Machine Learning 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 with state and action spaces that are orders of magnitude larger than that of standard AI benchmarks in robotics and games. In this talk, we will describe some of our latest learning based approaches to tackling such large-scale optimization problems. We will discuss our work on a new domain-transferable reinforcement learning (RL) method for optimizing chip placement [1], 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 power, performance, and area. We show that, in under six 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.