标题： 冰山：利用合成数据增强HLS建模 显示英文标题

标题： Iceberg: Enhancing HLS Modeling with Synthetic Data

摘要： 基于深度学习的高级综合（HLS）硬件设计预测模型通常难以泛化。 在本文中，我们研究了如何通过在合成数据上进行预训练来缩小这些模型的泛化能力差距，并引入了Iceberg，这是一种合成数据增强方法，可扩展大型语言模型（LLM）生成的程序和未见设计配置的弱标签。 我们的弱标签生成方法与上下文模型架构集成，使元学习能够从实际和接近的标签中学习。 当适应六个现实世界的应用时，Iceberg在少量示例的情况下将几何平均建模准确率提高了$86.4\%$，并在适应两个不同的测试数据集时实现了$2.47\times$和$1.12\times$更好的离线DSE性能。 我们的开源代码在此：https://github.com/UCLA-VAST/iceberg 显示英文摘要

摘要： Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap of these models through pretraining on synthetic data and introduce Iceberg, a synthetic data augmentation approach that expands both large language model (LLM)-generated programs and weak labels of unseen design configurations. Our weak label generation method is integrated with an in-context model architecture, enabling meta-learning from actual and proximate labels. Iceberg improves the geometric mean modeling accuracy by $86.4\%$ when adapt to six real-world applications with few-shot examples and achieves a $2.47\times$ and a $1.12\times$ better offline DSE performance when adapting to two different test datasets. Our open-sourced code is here: https://github.com/UCLA-VAST/iceberg