# ArtNet: Hierarchical Clustering-Based Artificial Netlist Generator for ML and DTCO Application

arXiv: https://arxiv.org/abs/2510.13582 · HTML: https://mlsanity.com/paper/2510.13582/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [D-VAE: A Variational Autoencoder for Directed Acyclic Graphs](https://mlsanity.com/paper/1904.11088/) (2019)
- [A Systematic Survey on Deep Generative Models for Graph Generation](https://mlsanity.com/paper/2007.06686/) (2020)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures](https://mlsanity.com/paper/1608.06037/) (2016)
- [DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models](https://mlsanity.com/paper/2305.16943/) (2023)
- [HYGENE: A Diffusion-based Hypergraph Generation Method](https://mlsanity.com/paper/2408.16457/) (2024)
- [LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation](https://mlsanity.com/paper/2411.02322/) (2024)
- [PROBE3.0: A Systematic Framework for Design-Technology Pathfinding with Improved Design Enablement](https://mlsanity.com/paper/2304.13215/) (2023)

## Abstract (arXiv)

In advanced nodes, optimization of power, performance and area (PPA) has become highly complex and challenging. Machine learning (ML) and design-technology co-optimization (DTCO) provide promising mitigations, but face limitations due to a lack of diverse training data as well as long design flow turnaround times (TAT). We propose ArtNet, a novel artificial netlist generator designed to tackle these issues. Unlike previous methods, ArtNet replicates key topological characteristics, enhancing ML model generalization and supporting broader design space exploration for DTCO. By producing realistic artificial datasets that moreclosely match given target parameters, ArtNet enables more efficient PPAoptimization and exploration of flows and design enablements. In the context of CNN-based DRV prediction, ArtNet's data augmentationimproves F1 score by 0.16 compared to using only the original (real) dataset. In the DTCO context, ArtNet-generated mini-brains achieve a PPA match up to 97.94%, demonstrating close alignment with design metrics of targeted full-scale block designs.