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。

跳过基准：使用生成机器学习生成系统级高级综合数据

Skip the Benchmark: Generating System-Level High-Level Synthesis Data  using Generative Machine Learning

Generalized zero-shot learning models (GZSL) aim to recognize samples from
seen or unseen classes using only samples from seen classes as training data.
During inference, GZSL methods are often biased towards seen classes due to the
visibility of seen class samples during training. Most current GZSL methods try
to learn an accurate projection function (from visual space to semantic space)
to avoid bias and ensure the effectiveness of GZSL methods. However, during
inference, the computation of distance will be important when we classify the
projection of any sample into its nearest class since we may learn a biased
projection function in the model. In our work, we attempt to learn a
parameterized Mahalanobis distance within the framework of VAEGAN (Variational
Autoencoder \& Generative Adversarial Networks), where the weight matrix
depends on the network's output. In particular, we improved the network
structure of VAEGAN to leverage the discriminative models of two branches to
separately predict the seen samples and the unseen samples generated by this
seen one. We proposed a new loss function with two branches to help us learn
the optimized Mahalanobis distance representation. Comprehensive evaluation
benchmarks on four datasets demonstrate the superiority of our method over the
state-of-the-art counterparts. Our codes are available at
this https URL

通过在 VAEGAN 框架中学习带权重矩阵的参数化马氏距离，我们提出了一种改进的网络结构来优化广义零样本学习模型，通过两个分支的鉴别模型分别预测已知样本和由已知样本生成的未知样本，实验结果显示我们的方法在四个数据集上优于现有最先进的方法。