标题： HLStrans：用于LLM驱动的C到HLS硬件代码合成的数据集 显示英文标题

标题： HLStrans: Dataset for LLM-Driven C-to-HLS Hardware Code Synthesis

摘要： 高级综合（HLS）使软件开发人员能够通过使用C/C++而不是传统的硬件描述语言来在更高抽象层次上描述和实现硬件，从而自动生成适合FPGA的设计。 然而，生成HLS代码与标准 C/C++显著不同：它不允许某些编码习惯，依赖于专用库，并且关键需要细粒度的转换和插入优化指令（伪指令）以实现高性能。 大型语言模型（LLMs）在自动化此类转换方面显示出潜力，但现有的开源数据集缺乏足够的复杂性和优化多样性。 为解决这一差距，我们引入了HLStrans数据集，这是一个包含137个不同真实程序的全面集合，每个程序都标注了多种从C到HLS的转换，产生超过23K个标记的设计变体。 这些包括广泛的伪指令和代码级优化。 我们在该数据集上对最先进的LLMs进行基准测试，以评估它们生成可综合的高性能HLS代码的能力。 作为一项持续努力的一部分，我们计划在规模和程序多样性方面扩展HLStrans数据集，进一步推动人工智能和硬件综合交叉领域的研究。 显示英文摘要

摘要： High-level synthesis (HLS) enables software developers to describe and implement hardware at a higher level of abstraction by using C/C++ instead of traditional hardware description languages to automatically generate FPGA-ready designs. However, generating HLS code significantly differs from standard C/C++: it disallows certain coding idioms, relies on specialized libraries, and critically requires fine-grained transformations and the insertion of optimization directives (pragmas) to achieve high performance. Large language models (LLMs) have shown promise in automating such transformations, yet existing open-source datasets lack sufficient complexity and optimization diversity. To address this gap, we introduce the HLStrans dataset, a comprehensive collection of 137 distinct real word programs, each annotated with a variety of C-to-HLS transformations that yield over 23K labeled design variants. These include a broad spectrum of pragmas and code-level optimizations. We benchmark state-of-the-art LLMs on this dataset to evaluate their ability to generate synthesizable, high-performance HLS code. As part of an ongoing effort, we plan to expand the HLStrans dataset in both scale and program variety, further empowering research at the intersection of AI and hardware synthesis.