RL-CCD: Concurrent Clock and Data Optimization using Attention-Based Self-Supervised Reinforcement Learning

Concurrent Clock and Data (CCD) optimization is a well-adopted approach in modern commercial tools that resolves timing violations using a mixture of clock skewing and delay fixing strategies. However, existing CCD algorithms are flawed. Particularly, they fail to prioritize violating endpoints for different optimization strategies correctly, leading to flow-wise globally sub-optimal results. In this paper, we overcome this issue by presenting RL-CCD, a Reinforcement Learning (RL) agent that selects endpoints for useful skew prioritization using the proposed EP-GNN, an endpoint-oriented Graph Neural Network (GNN) model, and a Transformer-based self-supervised attention mechanism. Experimental results on 19 industrial designs in 5 − 12nm technologies demonstrate that RL-CCD achieves up to 64% Total Negative Slack (TNS) reduction and 66.5% number of violating endpoints (NVE) improvement over the native implementation of a commercial tool.