Abstract
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.
Abstract (translated)
高级合成设计空间探索（HLS）是一种在HLS过程中广泛接受的方法，用于有效地探索Pareto最优和最优硬件解决方案。有许多HLS基准和数据集可供研究社区评估其方法论。然而，这些资源有限，可能不足以支持对复杂、多组件系统级探索。利用现有HLS基准生成新数据可能很费力，因为生成不同HLSD的设计和指令需要专业知识和时间。因此，在之前的工作中，使用合成数据来评估系统级HLSD是一种常见的做法。然而，合成数据的可靠性通常难以与真实数据相匹敌，导致对系统级HLSD的质量不确定性增加。 本文提出了一种名为Vaegan的新方法，采用生成机器学习来生成足够稳健的合成数据，以支持无法仅通过现有数据进行的复杂系统级HLSD实验。我们探讨并适应了这种任务，并使用最先进的 datasets 和 metrics 评估了我们的方法。我们比较了我们的方法与先前的作品，并证明了Vaegan有效地生成与真实分布高度相似的合成HLSD数据。
URL
https://arxiv.org/abs/2404.14754
