HLSDataset: Open-Source Dataset for ML-Assisted FPGA Design using High Level Synthesis

Machine Learning (ML) has been widely adopted in design exploration using high level synthesis (HLS) for faster resource, timing and power estimation at very early stages for FPGA-based design. To perform prediction accurately, high-quality and large-volume datasets are required for training ML models. However, the current datasets used in this domain are proprietary or limited in use, and practitioners have to generate their own dataset to train HLS-related ML models. This paper presents a dataset for ML-assisted FPGA design using HLS, called HLSDataset. The dataset is generated from widely used HLS C benchmarks including Polybench, Machsuite, CHStone and Rossetta. The Verilog samples are generated with a variety of directives including loop unroll, loop pipeline, and array partition to make sure optimized and realistic designs are covered. The total number of generated Verilog samples is nearly 9,000 per FPGA type. The dataset repository includes CSV (comma separated values) files containing both HLS and implementation metrics which can be easily consumed by ML model. We also include original C source code with directives, Verilog designs, post-HLS reports, post-implementation reports for each sample in the dataset, so that any metrics not present in the CSV can be easily extracted. In order to extend the dataset for future benchmarks, generation and extraction scripts are also provided. To demonstrate the effectiveness of our dataset, we undertake case studies to perform power estimation and resource usage estimation with ML models trained with our dataset. All the code and dataset are public at our github page
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https://github.com/UT-LCAIML4Accel-Dataset/tree/main/fpga_ml_dataset. We believe that HLSDataset can save valuable time for researchers by avoiding the tedious process of running tools, scripting and parsing files to generate the dataset, and enable them to spend more time where it counts, that is, in training ML models.