Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies
Authors: Jian Gao, Weidong Cao, Junyi Yang, Xuan Zhang
ICLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 16,817 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,901 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the remarkable generation performance of Analog Genie in broadening the variety of analog ICs, increasing the number of devices within a single design, and discovering unseen circuit topologies far beyond any prior arts. |
| Researcher Affiliation | Academia | 1 Northeastern University, 2 The George Washington University EMAIL {weidong.cao}@gwu.edu |
| Pseudocode | No | The paper describes methods in prose and includes theoretical proofs (Theorem 3.2.1) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code is available at https://github.com/xz-group/Analog Genie. |
| Open Datasets | No | For our open-sourced circuit dataset, we provide its statistics in Appendix A.1. We will make our code and dataset public on Github in the future. |
| Dataset Splits | Yes | During training, we first split the topology data set into train and validation sets with a 9 to 1 ratio. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using a 'GPT model' and 'Ngspice simulation infrastructure' but does not specify any version numbers for software dependencies. |
| Experiment Setup | Yes | Our Analog Genie model is a decoder-only transformer consisting of 6 hidden layers and 6 attention heads with 11.825 million parameters in total. The vocab size is 1029. The maximum sequence length is 1024. |