High-Level Synthesis (HLS) Design Space Exploration (DSE) is a widely accepted approach for efficiently exploring Pareto-optimal and optimal hardware solutions during the HLS process. Several HLS benchmarks and datasets are available for the research community to evaluate their methodologies. Unfortunately, these resources are limited and may not be sufficient for complex, multi-component system-level explorations. Generating new data using existing HLS benchmarks can be cumbersome, given the expertise and time required to effectively generate data for different HLS designs and directives. As a result, synthetic data has been used in prior work to evaluate system-level HLS DSE. However, the fidelity of the synthetic data to real data is often unclear, leading to uncertainty about the quality of system-level HLS DSE. This paper proposes a novel approach, called Vaegan, that employs generative machine learning to generate synthetic data that is robust enough to support complex system-level HLS DSE experiments that would be unattainable with only the currently available data. We explore and adapt a Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) for this task and evaluate our approach using state-of-the-art datasets and metrics. We compare our approach to prior works and show that Vaegan effectively generates synthetic HLS data that closely mirrors the ground truth's distribution.

翻译：高层次综合（HLS）设计空间探索（DSE）是一种在HLS过程中高效探索帕累托最优和最优硬件解决方案的广泛接受方法。研究社区可利用多个HLS基准测试和数据集来评估其方法论。然而，这些资源有限，可能不足以用于复杂的多组件系统级探索。利用现有HLS基准测试生成新数据可能繁琐，因为有效生成不同HLS设计和指令所需的数据需要大量的专业知识和时间。因此，先前的工作中已使用合成数据来评估系统级HLS DSE。但合成数据对真实数据的保真度通常不明确，导致对系统级HLS DSE质量的质疑。本文提出一种名为Vaegan的新方法，利用生成式机器学习生成足够鲁棒的合成数据，以支持仅凭现有数据无法实现的复杂系统级HLS DSE实验。我们探索并改编了变分自编码器（VAE）和生成对抗网络（GAN）用于此任务，并使用最先进的数据集和指标评估我们的方法。我们将我们的方法与先前工作进行比较，并表明Vaegan能有效生成与真实数据分布高度吻合的合成HLS数据。