LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation
Authors: Chen-Chia Chang, Wan-Hsuan Lin, Yikang Shen, Yiran Chen, Xin Zhang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that La MAGIC2 achieves 34% higher success rates under a tight tolerance of 0.01 and 10X lower MSEs compared to a prior method. La MAGIC2 also exhibits better transferability for circuits with more vertices with up to 58.5% improvement. These advancements establish La MAGIC2 as a robust framework for analog topology generation. |
| Researcher Affiliation | Collaboration | 1Duke University 2University of California, Los Angeles 3MITIBM Watson AI Lab 4IBM T. J. Watson Research Center. Correspondence to: Chen-Chia Chang <EMAIL>, Xin Zhang <EMAIL>. |
| Pseudocode | No | The paper describes methods and formulations but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code available at https: //github.com/turtleben/La MAGIC. |
| Open Datasets | Yes | We utilize the same dataset in La MAGIC (Chang et al., 2024). It contains 3, 4, 5-component circuits with 120k data points for training and 12k for evaluation. To assess the transferability of models to more complex circuits, the dataset has 76k unique 6-component circuits and split 9k data points for evaluation. |
| Dataset Splits | Yes | It contains 3, 4, 5-component circuits with 120k data points for training and 12k for evaluation. To assess the transferability of models to more complex circuits, the dataset has 76k unique 6-component circuits and split 9k data points for evaluation. In our experiments, we randomly select subsets of 500, 1k, and 2k 6-component circuits to fine-tune models initially trained on the 120k 3, 4, 5-component circuits. |
| Hardware Specification | Yes | Training runs on one NVIDIA V100 GPU using Adam W with the following hyperparameter: learning rate 3 10 4, cosine scheduler with 300 warm-up steps, batch size 128, L2 regularization 10 5, dropout 0.1, and epochs |
| Software Dependencies | Yes | We run simulator NGSPICE (Nenzi P, 2011) on each generated circuit to get its actual voltage conversion ratio and efficiency for real-world applications. Nenzi P, V. H. Ngspice users manual version 23., 2011. URL https://pkgs.fedoraproject.org/ repo/extras/ngspice/ngspice23-manual. pdf/eb0d68eb463a41a0571757a00a5b9f9d/ ngspice23-manual.pdf. |
| Experiment Setup | Yes | Training runs on one NVIDIA V100 GPU using Adam W with the following hyperparameter: learning rate 3 10 4, cosine scheduler with 300 warm-up steps, batch size 128, L2 regularization 10 5, dropout 0.1, and epochs |