# ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis

arXiv: https://arxiv.org/abs/2507.03255 · HTML: https://mlsanity.com/paper/2507.03255/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning](https://mlsanity.com/paper/2102.08138/) (2021)
- [High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing](https://mlsanity.com/paper/2201.06848/) (2022)
- [AutoDSE: Enabling Software Programmers to Design Efficient FPGA Accelerators](https://mlsanity.com/paper/2009.14381/) (2020)
- [DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis](https://mlsanity.com/paper/2502.18297/) (2025)
- [PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs](https://mlsanity.com/paper/2201.10114/) (2022)
- [HL-Pow: A Learning-Based Power Modeling Framework for High-Level Synthesis](https://mlsanity.com/paper/2009.00871/) (2020)
- [DB4HLS: A Database of High-Level Synthesis Design Space Explorations](https://mlsanity.com/paper/2101.00587/) (2021)
- [HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond](https://mlsanity.com/paper/2405.00820/) (2024)
- [Automatic Hardware Pragma Insertion in High-Level Synthesis: A Non-Linear Programming Approach](https://mlsanity.com/paper/2405.12304/) (2024)
- [Hierarchical Source-to-Post-Route QoR Prediction in High-Level Synthesis with GNNs](https://mlsanity.com/paper/2401.08696/) (2024)
- [HLSDataset: Open-Source Dataset for ML-Assisted FPGA Design using High Level Synthesis](https://mlsanity.com/paper/2302.10977/) (2023)

## Built on by (most cited first, 1 of 1)

- [DiffHLS: Differential Learning for High-Level Synthesis QoR Prediction with GNNs and LLM Code Embeddings](https://mlsanity.com/paper/2604.09240/) (2026)

## Abstract (arXiv)

High-Level Synthesis (HLS) plays a crucial role in modern hardware design by transforming high-level code into optimized hardware implementations. However, progress in applying machine learning (ML) to HLS optimization has been hindered by a shortage of sufficiently large and diverse datasets. To bridge this gap, we introduce ForgeHLS, a large-scale, open-source dataset explicitly designed for ML-driven HLS research. ForgeHLS comprises over 400k diverse designs generated from 846 kernels covering a broad range of application domains, consuming over 200k CPU hours during dataset construction. Each kernel includes systematically automated pragma insertions (loop unrolling, pipelining, array partitioning), combined with extensive design space exploration using Bayesian optimization. Compared to existing datasets, ForgeHLS significantly enhances scale, diversity, and design coverage. We further define and evaluate representative downstream tasks in Quality of Result (QoR) prediction and automated pragma exploration, clearly demonstrating ForgeHLS utility for developing and improving ML-based HLS optimization methodologies. The dataset and code are public at https://github.com/zedong-peng/ForgeHLS.