wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation

• URL: http://arxiv.org/abs/2511.05615v1
• Date: Thu, 06 Nov 2025 17:18:13 GMT
• Title: wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation
• Authors: Benjamin Hawks, Jason Weitz, Dmitri Demler, Karla Tame-Narvaez, Dennis Plotnikov, Mohammad Mehdi Rahimifar, Hamza Ezzaoui Rahali, Audrey C. Therrien, Donovan Sproule, Elham E Khoda, Keegan A. Smith, Russell Marroquin, Giuseppe Di Guglielmo, Nhan Tran, Javier Duarte, Vladimir Loncar,
• Abstract summary: We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation.<n>We also introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators.
• Score: 1.2929845407528824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 latency estimation, and its corresponding initial dataset of over 680,000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

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