Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow. However, the bulk of net length information is not available until cell placement, and hence it is a significant challenge to explicitly consider net length optimization in design stages prior to placement, such as logic synthesis. This work addresses this challenge by proposing a graph attention network method with customization, called Net2, to estimate individual net length before cell placement. Its accuracy-oriented version Net2a achieves about 15% better accuracy than several previous works in identifying both long nets and long critical paths. Its fast version Net2f is more than 1000 times faster than placement while still outperforms previous works and other neural network techniques in terms of various accuracy metrics.
翻译:净长度是在标准数字设计流程的各个阶段优化时间和功率的关键代用标准尺度。 但是,在细胞布置之前,大部分净长度信息是无法提供的,因此,明确考虑在定位之前的设计阶段(如逻辑合成)的净长度优化是一个重大挑战。这项工作通过提出一个定制化的图形关注网络方法(称为 Net2)来应对这一挑战,以估计细胞布置之前的个人净长度。其精确导向版本 Net2a 在识别长网和长关键路径方面比先前的几项工作准确率高约15%。 其快速版本 Net2f比定位速度快1000倍以上, 但仍在各种精确度指标方面超过先前的工程和其他神经网络技术。