论文标题
使用卷积编码器 - 模块网络的热和红外滴分析
Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks
论文作者
论文摘要
在设计周期内需要进行计算昂贵的温度和电网分析，以指导IC设计。本文采用基于编码器的生成（EDGE）网络来将这些分析映射到快速准确的图像到图像以及顺序到序列转换任务。该网络将功率图作为输入，并输出相应的温度或IR下降图。我们提出了两个网络：（i）Thermedge：一个静态和动态的全芯片温度估计器和（ii）IREDGE：基于输入功率，功率电网分布和动力垫分布模式的全芯片静态IR滴剂预测器。这些模型是无关的，只需对特定技术和包装解决方案进行一次培训。 Shupterged和Iredge被证明可以快速预测毫秒内的片上温度和IR滴轮廓（与需要数小时或更长时间的商业工具相比），并提供平均误差为0.6％和0.008％。
Computationally expensive temperature and power grid analyses are required during the design cycle to guide IC design. This paper employs encoder-decoder based generative (EDGe) networks to map these analyses to fast and accurate image-to-image and sequence-to-sequence translation tasks. The network takes a power map as input and outputs the corresponding temperature or IR drop map. We propose two networks: (i) ThermEDGe: a static and dynamic full-chip temperature estimator and (ii) IREDGe: a full-chip static IR drop predictor based on input power, power grid distribution, and power pad distribution patterns. The models are design-independent and must be trained just once for a particular technology and packaging solution. ThermEDGe and IREDGe are demonstrated to rapidly predict the on-chip temperature and IR drop contours in milliseconds (in contrast with commercial tools that require several hours or more) and provide an average error of 0.6% and 0.008% respectively.
