  Machine Learning (ML) has been widely adopted in design exploration using
high level synthesis (HLS) to give a better and faster performance, and
resource 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.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. 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 codes and dataset
are public at the github repo.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.
