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.

该论文提出了一种名为 Vaegan 的新方法，利用生成式机器学习生成足够强大以支持复杂系统级高级综合设计空间探索实验的合成数据，该方法使用变分自动编码器（VAE）和生成对抗网络（GAN）来进行任务，并使用先进的数据集和度量标准对方法进行评估，与之前的工作进行比较，表明 Vaegan 可以有效生成与真实数据分布接近的合成高级综合设计空间数据。