Zhanwen Zhou

Zhanwen Zhou, Hankz Hankui Zhuo, Xiaowu Zhang et al.

Routing is a crucial and time-consuming stage in modern design automation flow for advanced technology nodes. Great progress in the field of reinforcement learning makes it possible to use those approaches to improve the routing quality and efficiency. However, the scale of the routing problems solved by reinforcement learning-based methods in recent studies is too small for these methods to be used in commercial EDA tools. We introduce the XRoute Environment, a new reinforcement learning environment where agents are trained to select and route nets in an advanced, end-to-end routing framework. Novel algorithms and ideas can be quickly tested in a safe and reproducible manner in it. The resulting environment is challenging, easy to use, customize and add additional scenarios, and it is available under a permissive open-source license. In addition, it provides support for distributed deployment and multi-instance experiments. We propose two tasks for learning and build a full-chip test bed with routing benchmarks of various region sizes. We also pre-define several static routing regions with different pin density and number of nets for easier learning and testing. For net ordering task, we report baseline results for two widely used reinforcement learning algorithms (PPO and DQN) and one searching-based algorithm (TritonRoute). The XRoute Environment will be available at https://github.com/xplanlab/xroute_env.

Siqi Hong, Hankz Hankui Zhuo, Kebing Jin et al.

In retrosynthetic planning, the huge number of possible routes to synthesize a complex molecule using simple building blocks leads to a combinatorial explosion of possibilities. Even experienced chemists often have difficulty to select the most promising transformations. The current approaches rely on human-defined or machine-trained score functions which have limited chemical knowledge or use expensive estimation methods for guiding. Here we an propose experience-guided Monte Carlo tree search (EG-MCTS) to deal with this problem. Instead of rollout, we build an experience guidance network to learn knowledge from synthetic experiences during the search. Experiments on benchmark USPTO datasets show that, EG-MCTS gains significant improvement over state-of-the-art approaches both in efficiency and effectiveness. In a comparative experiment with the literature, our computer-generated routes mostly matched the reported routes. Routes designed for real drug compounds exhibit the effectiveness of EG-MCTS on assisting chemists performing retrosynthetic analysis.