--Machine learning (ML) has demonstrated significant promise in various physical design (PD) tasks. However, model gen-eralizability remains limited by the availability of high-quality, large-scale training datasets. Creating such datasets is often computationally expensive and constrained by IP . While very few public datasets are available, they are typically static, slow to generate, and require frequent updates. T o address these limitations, we present DALI-PD, a scalable framework for generating synthetic layout heatmaps to accelerate ML in PD research. DALI-PD uses a diffusion model to generate diverse layout heatmaps via fast inference in seconds. The heatmaps include power, IR drop, congestion, macro placement, and cell density maps. Using DALI-PD, we created a dataset comprising over 20,000 layout configurations with varying macro counts and placements. These heatmaps closely resemble real layouts and improve ML accuracy on downstream ML tasks such as IR drop or congestion prediction.

Electronic design automation (EDA), often known as computer-aided design (CAD), is a class of software tools used to create electronic systems like integrated circuits (ICs). EDA tools enable designers to create a design for large-scale integrated chips (VLSI) with billions of transistors. Due to the size and complexity of current electronic systems, EDA tools are crucial for VLSI design. The EDA research community has recently been actively investigating AI for IC methodologies to design cutting-edge chips, thanks to the explosion of artificial intelligence (AI) algorithms. Numerous studies have investigated machine learning-based solutions for cross-stage prediction tasks in the design cycle to promote speedier design convergence.

Electronic design automation (EDA) or computer-aided design (CAD) is a category of software tools for designing electronic systems, such as integrated circuits (ICs). With EDA tools, designers can finish the design flow of very large scale integrated (VLSI) chips with billions of transistors. EDA tools are essential to modern VLSI design due to the large scale and high complexity of electronic systems. Recently, with the boom of artificial intelligence (AI) algorithms, the EDA community is actively exploring AI for IC techniques for the design of advanced chips. Many studies have explored machine learning (ML) based techniques for cross-stage prediction tasks in the design flow to achieve faster design convergence.

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.