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...

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Vydáno v:2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6
Hlavní autoři: Lu, Yi-Chen, Chan, Wei-Ting, Guo, Deyuan, Kundu, Sudipto, Khandelwal, Vishal, Lim, Sung Kyu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 09.07.2023
Témata:
Charge coupled devices
Delays
Design automation
Graph neural networks
Optimization
Reinforcement learning
Transformers
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Shrnutí: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.
DOI:10.1109/DAC56929.2023.10248008