Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits
Authors: Chen-Chia Chang, Yikang Shen, Shaoze Fan, Jing Li, Shun Zhang, Ningyuan Cao, Yiran Chen, Xin Zhang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that La MAGIC achieves a success rate of up to 96% under a strict tolerance of 0.01. We also examine the scalability and adaptability of La MAGIC, specifically testing its performance on more complex circuits. |
| Researcher Affiliation | Collaboration | 1IBM T. J. Watson Research Center 2Duke University 3MIT-IBM Watson AI Lab 4New Jersey Institute of Technology 5University of Notre Dame. |
| Pseudocode | No | The paper describes methods and formulations but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement or link is provided for open-sourcing the code related to the described methodology. |
| Open Datasets | No | In our main experiment (Section 5.2), we construct a dataset by randomly sampling topologies of 3, 4, and 5-component circuits. This range was chosen to encapsulate the varying degrees of complexity typical in power converter circuits, thereby ensuring that our model will be learned to handle a variety of design scenarios. |
| Dataset Splits | No | In total, we randomly split around 120k data points for training and 12k for evaluation. |
| Hardware Specification | Yes | Our experiment runs on a machine with one NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions NGSPICE and Flan-T5 but does not provide specific version numbers for these or other software dependencies like Python, PyTorch, or specific libraries. |
| Experiment Setup | Yes | The hyperparameters of the LM training are detailed as follows: We perform training for 120 epochs using Adam W optimizer with a learning rate of 3 10 4 with a cosine scheduler using 300 warmup steps, a batch size of 128, and a L2 regularization strength of 10 5. |