Applying reinforcement learning to learn best net to rip and re-route in global routing

Author:

Gandhi Upma1ORCID,Aghaeekiasaraee Erfan1ORCID,Sahir 2ORCID,Mousavi Payam2ORCID,Bustany Ismail S. K.3ORCID,Taylor Mathew E.4ORCID,Behjat Laleh5ORCID

Affiliation:

1. Electrical and Software Engineering, University of Calgary, Calgary, Canada

2. Alberta Machine Intelligence Institute, Edmonton, Canada

3. Advanced Micro Devices Inc, San Jose, United States

4. University of Alberta, Edmonton, Canada and Alberta Machine Intelligence Institute, Edmonton, Canada

5. University of Calgary, Calgary, Canada

Abstract

Physical designers typically employ heuristics to solve challenging problems in global routing. However, these heuristic solutions are not adaptable to the ever-changing fabrication demands, and the experience and creativity of designers can limit their effectiveness. Reinforcement learning (RL) is an effective method to tackle sequential optimization problems due to its ability to adapt and learn through trial and error. Hence, RL can create policies that can handle complex tasks. This work presents an RL framework for global routing that incorporates a self-learning model called RL-Ripper. The primary function of RL-Ripper is to identify the best nets that need to be ripped and rerouted in order to decrease the number of total short violations. In this work, we show that the proposed RL-Ripper framework’s approach can reduce the number of short violations for ISPD 2018 benchmarks when compared to the state-of-the-art global router CUGR. Moreover, RL-Ripper reduced the total number of short violations after the first iteration of detailed routing over the baseline while being on par with the wirelength, VIA, and runtime. The proposed framework’s major impact is providing a novel learning-based approach to global routing that can be replicated for newer technologies.

Funder

NSERC

Intelligent Robot Learning (IRL) Lab at the University of Alberta

Alberta Machine Intelligence Institute

NSERC and Compute Canada

Publisher

Association for Computing Machinery (ACM)

Reference41 articles.

1. Neural networks and deep learning;Aggarwal Charu C.;Springer,2018

2. CRP2.0: A Fast and Robust Cooperation between Routing and Placement in Advanced Technology Nodes

3. Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. arxiv:cs.LG/1606.01540

4. NTHU-Route 2.0: A Robust Global Router for Modern Designs

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