XRoute Environment: A Novel Reinforcement Learning Environment for Routing

by   Zhanwen Zhou, 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.


page 4

page 8


Google Research Football: A Novel Reinforcement Learning Environment

Recent progress in the field of reinforcement learning has been accelera...

5G Routing Interfered Environment

5G is the next-generation cellular network technology, with the goal of ...

Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

Applying Machine Learning (ML) techniques to design and optimize compute...

Design and implementation of an environment for Learning to Run a Power Network (L2RPN)

This report summarizes work performed as part of an internship at INRIA,...

rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks

Training reinforcement learning agents at solving a given task is highly...

Attention Routing: track-assignment detailed routing using attention-based reinforcement learning

In the physical design of integrated circuits, global and detailed routi...

Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization

Circuit routing has been a historically challenging problem in designing...

Please sign up or login with your details

Forgot password? Click here to reset