利用扩散模型提升数字VLSI电路ML模型精度：合成数据生成研究
Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation
AI总结：
本研究利用扩散模型为数字VLSI电路生成合成数据，以增强机器学习模型在性能评估、设计和测试中的准确性，验证了数据增强的有效性。
摘要：
生成式人工智能在过去几年中取得了显著增长，其中扩散模型在图像生成方面处于最先进水平。本研究探讨了利用扩散模型为电子电路生成人工数据，以提升后续机器学习模型在性能评估、设计和测试等任务中的准确性，尤其是在训练数据通常非常有限的情况下。我们利用HSPICE设计环境中的22nm CMOS技术节点进行仿真，以获取用于我们提出的扩散模型的代表性真实训练数据。我们的结果表明，使用扩散模型生成的合成数据与真实数据具有高度相似性。我们验证了生成数据的质量，并证明数据增强在数字电路VLSI设计的预测分析中确实有效。
英文摘要：
Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation. This study investigates the use of diffusion models in generating artificial data generation for electronic circuits for enhancing the accuracy of subsequent machine learning models in tasks such as performance assessment, design, and testing when training data is usually known to be very limited. We utilize simulations in the HSPICE design environment with 22nm CMOS technology nodes to obtain representative real training data for our proposed diffusion model. Our results demonstrate the close resemblance of synthetic data using diffusion model to real data. We validate the quality of generated data, and demonstrate that data augmentation certainly effective in predictive analysis of VLSI design for digital circuits.
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