HLS Benchmarking Dataset

该数据集旨在预测数字设计组件的有效性、运行延迟以及各种利用率。同时，数据集包含了提示信息，并允许对高级综合领域的大型语言模型进行基准测试任务。任务包括分类和回归任务，旨在区分有效与无效的设计，并预测性能指标。

This dataset is developed to predict the effectiveness, operational latency, and multiple utilization metrics of digital design components. Furthermore, it incorporates prompt data and supports benchmarking tasks for large language models (LLMs) in the domain of high-level synthesis. The supported tasks include both classification and regression: the classification task is used to distinguish valid from invalid designs, and the regression task targets the prediction of performance metrics.