# CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)

arXiv: https://arxiv.org/abs/2208.01040 · HTML: https://mlsanity.com/paper/2208.01040/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 2 of 2)

- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [MAVIREC: ML-Aided Vectored IR-DropEstimation and Classification](https://mlsanity.com/paper/2012.10597/) (2020)

## Built on by (most cited first, 11 of 11)

- [CktGNN: Circuit Graph Neural Network for Electronic Design Automation](https://mlsanity.com/paper/2308.16406/) (2023)
- [DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis](https://mlsanity.com/paper/2502.18297/) (2025)
- [HLSDataset: Open-Source Dataset for ML-Assisted FPGA Design using High Level Synthesis](https://mlsanity.com/paper/2302.10977/) (2023)
- [PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction](https://mlsanity.com/paper/2403.18569/) (2024)
- [HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction](https://mlsanity.com/paper/2305.05374/) (2023)
- [EDALearn: A Comprehensive RTL-to-Signoff EDA Benchmark for Democratized and Reproducible ML for EDA Research](https://mlsanity.com/paper/2312.01674/) (2023)
- [R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII](https://mlsanity.com/paper/2604.08810/) (2026)
- [Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation](https://mlsanity.com/paper/2605.08291/) (2026)
- [ParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits](https://mlsanity.com/paper/2607.23225/) (2026)
- [CARVE: Breaking Data Barriers in Chip Placement by Harnessing Reusable Expertise](https://mlsanity.com/paper/2609.33106/) (2026)
- [Trinity: One Differentiable Physics for Training, Refining and Scoring Generative Floorplanners](https://mlsanity.com/paper/2610.04957/) (2026)

## Abstract (arXiv)

The electronic design automation (EDA) community has been actively exploring machine learning (ML) for very large-scale integrated computer-aided design (VLSI CAD). Many studies explored learning-based techniques for cross-stage prediction tasks in the design flow to achieve faster design convergence. Although building ML models usually requires a large amount of data, most studies can only generate small internal datasets for validation because of the lack of large public datasets. In this essay, we present the first open-source dataset called CircuitNet for ML tasks in VLSI CAD.