# DB4HLS: A Database of High-Level Synthesis Design Space Explorations

arXiv: https://arxiv.org/abs/2101.00587 · HTML: https://mlsanity.com/paper/2101.00587/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2021.

## Built on by (most cited first, 8 of 8)

- [HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond](https://mlsanity.com/paper/2405.00820/) (2024)
- [LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning](https://mlsanity.com/paper/2504.21187/) (2025)
- [AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs](https://mlsanity.com/paper/2403.10686/) (2024)
- [wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation](https://mlsanity.com/paper/2511.05615/) (2025)
- [A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration](https://mlsanity.com/paper/2111.14767/) (2021)
- [Skip the Benchmark: Generating System-Level High-Level Synthesis Data using Generative Machine Learning](https://mlsanity.com/paper/2404.14754/) (2024)
- [ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis](https://mlsanity.com/paper/2507.03255/) (2025)
- [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) frameworks allow to easily specify a large number of variants of the same hardware design by only acting on optimization directives. Nonetheless, the hardware synthesis of implementations for all possible combinations of directive values is impractical even for simple designs. Addressing this shortcoming, many HLS Design Space Exploration (DSE) strategies have been proposed to devise directive settings leading to high-quality implementations while limiting the number of synthesis runs. All these works require considerable efforts to validate the proposed strategies and/or to build the knowledge base employed to tune abstract models, as both tasks mandate the syntheses of large collections of implementations. Currently, such data gathering is performed ad-hoc, a) leading to a lack of standardization, hampering comparisons between DSE alternatives, and b) posing a very high burden to researchers willing to develop novel DSE strategies. Against this backdrop, we here introduce DB4HLS, a database of exhaustive HLS explorations comprising more than 100000 design points collected over 4 years of synthesis time. The open structure of DB4HLS allows the incremental integration of new DSEs, which can be easily defined with a dedicated domain-specific language. We think that of our database, available at https://www.db4hls.inf.usi.ch/, will be a valuable tool for the research community investigating automated strategies for the optimization of HLS-based hardware designs.