DeepLayout: Learning Neural Representations of Circuit Placement Layout
Authors: Yuxiang Zhao, Zhuomin Chai, Xun Jiang, Qiang Xu, Runsheng Wang, Yibo Lin
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on large-scale industrial datasets, demonstrating that Deep Layout surpasses state-of-the-art (SOTA) methods specialized for individual tasks on two crucial layout quality assessment benchmarks. The experiment results underscore the framework s robust capability to learn the intrinsic properties of circuits. |
| Researcher Affiliation | Collaboration | 1Peking University 2National Technology Innovation Center for EDA 3Wuhan University 4The Chinese University of Hong Kong 5Institute of Electronic Design Automation, Wuxi, China 6Beijing Advanced Innovation Center for Integrated Circuits . Correspondence to: Yibo Lin <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Layout-oriented Masking Algorithm. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology, nor does it include a link to a code repository. While it mentions using a public dataset, it doesn't extend to the code. |
| Open Datasets | Yes | We conduct experiments on Circuit Net (Chai et al., 2022; 2023), a large-scale public dataset of IC designs for realworld industrial applications. |
| Dataset Splits | Yes | In our experimental setup, the pre-training set contains four designs (RISCY-a, RISCY-b, RISCY-FPU-a, RISCY-FPU-b), totaling over 6,000 samples that only utilize the raw input data to represent a large corpus of unlabeled samples. The fine-tuning and test sets each introduce two additional designs, zero-riscy-a and zero-riscy-b. Specifically, the fine-tuning set comprises a small amount of labeled data configured as 5, 10, or 20 samples while the test set contains 100 samples. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions "deep-learning methodologies" and "AI methods" but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Table 2. Pre-training and downstream learning parameters, Pred and Esti are abbreviation of Prediction and Estimation. Tasks Lr Epoch Weight Decay Decoder Pre-training 4e-3 100 1e-2 U-Net + MLP Congestion Pred. 3e-4 50 1e-4 U-Net Wirelength Esti. 4e-4 50 0 MLP |