English(EN) Amortized Neural Optimization for Pre-Layout Signal Integrity Design Space Exploration using Differentiable Surrogates

新AI方法将信号完整性设计时间缩短几个数量级

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-05 17:13

研究人员开发了一种名为摊销神经网络优化（ANO）的新方法，以加速电子电路中信号完整性的设计空间探索。该方法使用可微分神经网络离线学习优化策略，消除了设计阶段迭代计算的需要。与传统方法相比，ANO可以实现三个到四个数量级的加速，将计算密集型的信号完整性优化转变为实时过程。 AI

影响通过实现实时信号完整性分析和优化，加速电子设计。

排序理由该集群包含一篇详细介绍新颖方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Julian With\"oft, Werner John, Emre Ecik, Ralf Br\"uning, J\"urgen G\"otze · 2026-06-08 04:00

使用可微分代理进行预布局信号完整性设计空间探索的摊销神经优化

arXiv:2606.07463v1 Announce Type: cross Abstract: Pre-layout design space exploration (DSE) for high-speed signal integrity (SI) analysis is often limited by the computational cost of simulations and iterative optimization algorithms within modern electronic design automation (ED…
arXiv cs.LG TIER_1 English(EN) · Jürgen Götze · 2026-06-05 17:13

使用可微分代理进行预布局信号完整性设计空间探索的摊销神经优化

Pre-layout design space exploration (DSE) for high-speed signal integrity (SI) analysis is often limited by the computational cost of simulations and iterative optimization algorithms within modern electronic design automation (EDA) workflows. While machine learning surrogate mod…