Masala-CHAI: A Large-Scale SPICE Netlist Dataset for Analog Circuits by Harnessing AI

Abstract

Masala-CHAI is the first fully automated framework leveraging large language models (LLMs) to generate Simulation Programs with Integrated Circuit Emphasis (SPICE) netlists. It addresses a long-standing challenge in automating netlist generation for analog circuits within circuit design automation. Automating this workflow could accelerate the creation of finetuned LLMs for analog circuit design and verification. We identify key challenges in this automation and evaluate the multi-modal capabilities of state-of-the-art LLMs, particularly GPT-4, to address these issues. We propose a three-step workflow to overcome current limitations: labeling analog circuits, prompt tuning, and netlist verification. This approach aims to create an end-to-end SPICE netlist generator from circuit schematic images, tackling the long-standing hurdle of accurate netlist generation. Our framework demonstrates significant performance improvements, tested on approximately 2,100 schematics of varying complexity. We open-source this solution for community-driven development.

For full paper, use this link: https://arxiv.org/abs/2411.14299

File and Folder Description

• ./hough/ : Folder containing scripts to use Hough Transform for net detection. Due to large size of the files, download the hough/ content from this Google Drive link: https://drive.google.com/file/d/1mTwWWSMsYwhJW-GfKKVm21Lm5lVtMJ1y/view?usp=sharing
• ./models/ : Folder containing scripts for YOLOv8-based circuit component detection.
• ./sample-images/ : Folder containing sample images to run the Auto-SPICE netlist generator.
• ./trained_checkpoints/ : Contains checkpoint file for YOLOv8 model after training.
• ./utils/ : Supporting scripts for various components of the Auto-SPICE pipeline.
• ./Dataset/ : Folder containing dataset of the images with schematics.
  • This contains images from AMSNet repo as well.
  • Arranged across different data_* folder depending upon their sources.
• main.py : Main script that runs the entire pipeline.
• run.py : Script to be called for generating netlists for sample images.
• environment.yml: Requirements file for creating conda environment
• visualize.ipynb: Jupyter notebook for visualizing output of Autospice for a given circuit diagram

Steps to Run the Framework

1. Clone the repository and navigate into the repository:

   git clone <repository_url>
   cd <repository_name>

2. Create a Conda environment:

   conda env create -f environment.yml

3. Activate the Conda environment:

   conda activate autospice_env

4. Add sample images: Place your sample images in the ./sample-images/ folder.

5. Run the pipeline:

   python run.py --src ./sample-images/ --tgt ./sample-output --api_key <openai_api_key>
   where - 
   - `--src` : Directory path to the sample images.
   - `--tgt` : Output directory path for the generated netlists.
   - `--api_key` : Your OpenAI API key for using GPT-4
   

Steps to Run circuit detection using Molmo (https://huggingface.co/allenai/Molmo-72B-0924)

1. Ensure the availability of 4 A100 40 GB GPUs
2. Run cells in molmo-objdetection.ipynb for loading a sample screenshot of a textbook page and run circuit detection

Output Files Description

For each sample circuit, the output consists of a number of files to help the user understand the output of various components in the pipeline:

1. scanned_circuit.png: Copy of the original circuit diagram.
2. detected_components.png, component_removed_circuit.png, components_description.txt: Output of the YOLOv8 component detection module:
   • detected_components.png: Components marked with bounding boxes.
   • component_removed_circuit.png: Components replaced with white spaces.
   • components_description.txt: Text file containing the description of the detected components.
3. nodes_terminals.png, connections_descriptions.txt, nodes_description.txt: Detected nodes in the circuit using Hough Transform:
   • nodes_terminals.png: Detected nodes in the circuit.
   • connections_descriptions.txt: Text file containing descriptions of various connections.
   • nodes_description.txt: Text file containing the description of various nodes in the circuit.
4. text_and_comp_removed_circuit.png: Original circuit diagram after removing all text content and detected circuit components.
5. rebuilt_circuit.png: Original circuit diagram overlaid with components and nodes.
6. original_withComponentsAndLineLabels.png, original_withLineLabels.png: Used for better visualization of the model output.
7. sample_statistics.json: Dictionary describing types of components in the circuit, along with node and net information.
8. spice.txt: Final generated SPICE netlist for the circuit diagram.

We also provide a helpful visualization of model output using a jupyer notebook: visualize.ipynb

Citing Auto-SPICE

If you use Auto-SPICE in your research or project, please cite it using the following BibTeX entry:

@misc{bhandari2024masalachailargescalespicenetlist,
      title={Masala-CHAI: A Large-Scale SPICE Netlist Dataset for Analog Circuits by Harnessing AI}, 
      author={Jitendra Bhandari and Vineet Bhat and Yuheng He and Siddharth Garg and Hamed Rahmani and Ramesh Karri},
      year={2024},
      eprint={2411.14299},
      archivePrefix={arXiv},
      primaryClass={cs.AR},
      url={https://arxiv.org/abs/2411.14299}, 
}