标题： ForgeHLS：一个大规模的开源数据集用于高级综合 显示英文标题

标题： ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis

摘要： 高级综合（HLS）在现代硬件设计中起着至关重要的作用，通过将高级代码转换为优化的硬件实现。然而，将机器学习（ML）应用于HLS优化方面的进展受到了足够大且多样化的数据集短缺的阻碍。为了弥补这一差距，我们引入了ForgeHLS，这是一个大规模的开源数据集，专门用于ML驱动的HLS研究。ForgeHLS包含从覆盖广泛应用领域的536个内核生成的超过400,000种多样化设计。每个内核包括系统自动插入的pragma（循环展开、流水线、数组分区），结合使用贝叶斯优化进行广泛的设计器空间探索。与现有数据集相比，ForgeHLS显著提升了规模、多样性和设计覆盖率。我们进一步定义并评估了代表性的下游任务，例如结果质量（QoR）预测和自动pragma探索，明确展示了ForgeHLS在开发和改进基于ML的HLS优化方法中的实用性。 显示英文摘要

摘要： High-Level Synthesis (HLS) plays a crucial role in modern hardware design by transforming high-level code into optimized hardware implementations. However, progress in applying machine learning (ML) to HLS optimization has been hindered by a shortage of sufficiently large and diverse datasets. To bridge this gap, we introduce ForgeHLS, a large-scale, open-source dataset explicitly designed for ML-driven HLS research. ForgeHLS comprises over 400,000 diverse designs generated from 536 kernels covering a broad range of application domains. Each kernel includes systematically automated pragma insertions (loop unrolling, pipelining, array partitioning), combined with extensive design space exploration using Bayesian optimization. Compared to existing datasets, ForgeHLS significantly enhances scale, diversity, and design coverage. We further define and evaluate representative downstream tasks, such as Quality of Result (QoR) prediction and automated pragma exploration, clearly demonstrating ForgeHLS's utility for developing and improving ML-based HLS optimization methodologies.