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 | Conference PDF | Archive PDF | Plain Text | 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. |