Physical design tools have complex workflows with many different ways of optimizing power, performance, and area (PPA) out of a large number of options and hyperparameters in different engines and functionalities. Black-box optimization techniques are widely adopted to automate quality-of-result (QoR) exploration. Such exploration often proves impractical in real-world customer environments due to high computational demands, lengthy exploration cycles, and the need for large parallel jobs. To reduce the exploration space for viable compute resource requirements, we propose a novel design methodology to enable transferable learning by incorporating design insights crafted on top of physical design experts’ experience and streamlining QoR exploration as a sequence generation task for best recipe selection. We apply language model-inspired alignment techniques to learn the ranking of different recipe sets, enabling our model to generalize beyond known-good manually tuned expert design recipes. Extensive evaluations demonstrate our method’s superior QoRs and runtime performance on unseen industrial designs and rigorous benchmarks.

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.