fastmachinelearning/wa-hls4ml-projects

Use restrictions

The declared licence (cc-by-nc-4.0) is classified as having use restrictions.

Licence facts reported by the dataset and its declared sources. Not legal advice, and not a grant of permission to use the dataset.

Evidence

• Declared licence: cc-by-nc-4.0 (from the tag)
• Licence files: no licence file at the top level of the repository
• Access gated on Hugging Face: no

Declared sources

No source datasets listed in the card metadata.

Signal

See all datasets marked Use restrictions.

Tasks

• text-generation
• feature-extraction
• question-answering
• summarization
• tabular-regression
• tabular-classification

Citation text from the card

@article{10.1145/3787490,
    author = {Hawks, Benjamin and Weitz, Jason and Demler, Dmitri and Tame-Narvaez, Karla and Plotnikov, Dennis and Rahimifar, Mohammad Mehdi and Rahali, Hamza Ezzaoui and Therrien, Audrey C. and Sproule, Donovan and Khoda, Elham E. and Smith, Keegan A. and Marroquin, Russell and Di Guglielmo, Giuseppe and Tran, Nhan and Duarte, Javier and Loncar, Vladimir},
	title = {wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation},
	year = {2026},
	issue_date = {June 2026},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	volume = {19},
    number = {2},
	issn = {1936-7406},
	url = {https://doi.org/10.1145/3787490},
	doi = {10.1145/3787490},
	abstract = {As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multiple efforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latenc

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