  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.
