Machine-Learning-Based Early-Stage Timing Prediction in SoC Physical Design

被引：0

作者：

Bai, Lida ^{[1
]}

Chen, Lan ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China

来源：

2018 14TH IEEE INTERNATIONAL CONFERENCE ON SOLID-STATE AND INTEGRATED CIRCUIT TECHNOLOGY (ICSICT) | 2018年

关键词：

Machine learning; Timing closure; Ensemble learning;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Timing closure is essential in SoC physical design. In this paper, machine learning models for timing prediction after floorplan are established. In the models, the features are selected and abstracted by analyzing these parameters from gate-level netlist, constraint files, and floorplan files. The classic machine learning algorithms, such as neural network, support vector machine (SVM), and ensemble machine learning, are explored. The corresponding regression models are applied to predict the timing of SoC. The testcase constructed by open source IP core is used to verify the proposed idea. The results show that the hybrid ensemble learning model has the best prediction performance among various learning models evaluated in this paper.

引用

页码：1383 / 1385

页数：3