FanoutNet: A Neuralized PCB Fanout Automation Method Using Deep Reinforcement Learning
Authors: Haiyun Li, Jixin Zhang, Ning Xu, Mingyu Liu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on real-world industrial PCB benchmarks demonstrate that our approach achieves 100% routability in all industrial cases and improves wire length by an average of 6.8%, which makes a significant improvement compared with the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1 School of Information Engineering, Wuhan University of Technology, Wuhan, China 2 School of Computer Science, Hubei University of Technology, Wuhan, China 3 Wuhan Research Institute, Huawei Device Co., Ltd., Wuhan, China |
| Pseudocode | No | The paper includes architectural diagrams (Figure 2) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. It only provides a link to a dataset: 'PCB dataset was acquired at https://github.com/aspdacsubmission-pcb-layout/PCBBenchmarks/'. |
| Open Datasets | Yes | We build two benchmark datasets consisting of eleven open-source PCB cases1 and five industrial PCB cases...1PCB dataset was acquired at https://github.com/aspdacsubmission-pcb-layout/PCBBenchmarks/ |
| Dataset Splits | No | The paper mentions 'Pre-Routability (Pre-RT) to compute approximate routability for RL training' but does not provide specific dataset split information (percentages, sample counts, or explicit methodology) for training, validation, or testing splits. |
| Hardware Specification | Yes | The experiments are performed on a 64-bit Windows workstation with AMD Ryzen 7 5800X 8-Core Processor 3.79 GHz, and 64 GB RAM. |
| Software Dependencies | No | The paper states 'Our method is implemented in Python with Pytorch for reinforcement learning, and C++ with the mingw64 compiler for PCB router,' but does not provide specific version numbers for PyTorch or the mingw64 compiler, which is necessary for reproducibility. |
| Experiment Setup | Yes | Our PPO hyperparameters, K epochs and the learning rate of policy and value model, are 20, 3e-4 and 1e-3, respectively. |