GeniusRoute: A New Analog Routing Paradigm Using Generative Neural Network Guidance
GeniusRoute: A New Analog Routing Paradigm Using Generative Neural Network Guidance
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DOI：
10.1109/iccad45719.2019.8942164
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Keren Zhu;Mingjie Liu;Yibo Lin;Biying Xu;Shaolan Li;Xiyuan Tang;Nan Sun;D. Pan
中科院分区：
文献类型：
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作者：
Keren Zhu;Mingjie Liu;Yibo Lin;Biying Xu;Shaolan Li;Xiyuan Tang;Nan Sun;D. Pan
由于敏感的布局依赖效应和不同的性能指标，模拟布线自动化难以推广到性能驱动的布局综合中。现有的研究提出了一些针对特定性能指标的启发式布局约束。然而，以前的框架无法自动地将路由与人类智能结合起来。本文提出了一种新颖的、全自动的模拟路由范例，它利用机器学习来提供路由指导，模仿复杂的手动布局方法。实验表明，本文提出的方法比现有的方法有了显著的改进，在能够推广到不同功能的电路的同时，也取得了与手工布局相当的性能。
Due to sensitive layout-dependent effects and varied performance metrics, analog routing automation for performance-driven layout synthesis is difficult to generalize. Existing research has proposed a number of heuristic layout constraints targeting specific performance metrics. However, previous frameworks fail to automatically combine routing with human intelligence. This paper proposes a novel, fully automated, analog routing paradigm that leverages machine learning to provide routing guidance, mimicking the sophisticated manual layout approaches. Experiments show that the proposed methodology obtains significant improvements over existing techniques and achieves competitive performance to manual layouts while being capable of generalizing to circuits of different functionality.