计算机科学
超大规模集成
架空(工程)
设计流量
依赖关系(UML)
并行计算
物理设计
加速度
向前看
电子设计自动化
计算机体系结构
集成电路设计
计算机工程
嵌入式系统
电路设计
算法
人工智能
物理
经典力学
操作系统
作者
Xun Jiang,Zizheng Guo,Zhuomin Chai,Yuxiang Zhao,Yibo Lin,Runsheng Wang,Ru Huang
标识
DOI:10.1109/iccad57390.2023.10323938
摘要
Routability and timing are two critical metrics for modern VLSI circuits. With increasing design complexity and continuous shrinking of technology nodes, optimizing routability and timing become extremely expensive due to high computational overhead for analysis. It is reported that conventional CPU-based parallelization strategies can no longer scale beyond 8–16 threads. In this talk, we introduce how to accelerate routability and timing optimization leveraging AI-enabled GPU acceleration. To break the inter-stage information dependency in conventional physical design flow, we build AI for EDA models with an open-source dataset, CircuitNet, to enable ultrafast design optimization on GPU. We hope our study can shed lights to future development of EDA tools with AI-enabled heterogenity.
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