论文标题
太大而失败了？活跃的几次学习指导逻辑综合
Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis
论文作者
论文摘要
产生亚最佳合成转化序列（“合成食谱”）是逻辑合成中的重要问题。手动制作的合成食谱的质量较差。最先进的机器学习（ML）致力于生成综合食谱，因为需要从头开始对模型进行培训，因此，使用耗时的合成运行来收集培训数据。我们提出了一种新的方法，即公牛眼，该方法对过去的合成数据进行了预先训练的模型，以准确预测看不见的Netlist的合成配方的质量。这种方法比最先进的机器学习方法实现了2x-10倍的运行时间改进和更好的增加质量（QOR）。
Generating sub-optimal synthesis transformation sequences ("synthesis recipe") is an important problem in logic synthesis. Manually crafted synthesis recipes have poor quality. State-of-the art machine learning (ML) works to generate synthesis recipes do not scale to large netlists as the models need to be trained from scratch, for which training data is collected using time consuming synthesis runs. We propose a new approach, Bulls-Eye, that fine-tunes a pre-trained model on past synthesis data to accurately predict the quality of a synthesis recipe for an unseen netlist. This approach on achieves 2x-10x run-time improvement and better quality-of-result (QoR) than state-of-the-art machine learning approaches.
