AI-Assisted EDA: Auto-Placement and Routing with RL
DOI:
https://doi.org/10.15662/IJRAI.2023.0602002Keywords:
Electronic Design Automation (EDA), Auto-Placement, Routing, Reinforcement Learning (RL), Deep Q-Network (DQN), Policy Gradient, Actor-Critic, Chip Design Optimization, Congestion Control, Timing ClosureAbstract
Electronic Design Automation (EDA) plays a critical role in modern integrated circuit (IC) design, particularly in placement and routing, which significantly impact the performance, power, and area of chips. Traditionally, placement and routing have relied on heuristic algorithms and manual tuning, which are time-consuming and struggle with the increasing complexity of modern designs. Recently, Artificial Intelligence (AI), especially Reinforcement Learning (RL), has emerged as a promising approach to automate and optimize these tasks more effectively. This paper explores AI-assisted EDA with a focus on auto-placement and routing using RL. Reinforcement learning, by enabling an agent to learn optimal policies through interactions with the environment, offers a dynamic and adaptive solution to the combinatorial optimization problems inherent in placement and routing. The paper discusses key RL algorithms applied to placement and routing tasks, including Deep Q-Networks (DQN), Policy Gradient methods, and Actor-Critic architectures. We review recent advances where RL agents learn to place standard cells and macros with considerations for congestion, timing, and power constraints. Similarly, RL-based routing algorithms adaptively find optimal wire paths that minimize delay and crosstalk. Integrations of RL with traditional EDA tools and constraints handling are also analyzed. Experimental results indicate that RL-assisted approaches can outperform classical heuristics by reducing wirelength, timing violations, and congestion while decreasing manual intervention. However, challenges such as state space explosion, reward shaping, and training time remain. The paper concludes by discussing future directions, including multi-agent RL, transfer learning across chip designs, and hybrid approaches combining RL with conventional EDA methods to improve scalability and robustness. This comprehensive review demonstrates the transformative potential of RL in enhancing auto-placement and routing, paving the way for more efficient and intelligent chip design automation.
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