计算机工程与科学2024，Vol.46Issue(6)：993-1000,8.DOI:10.3969/j.issn.1007-130X.2024.06.006
基于遗传算法的晶圆级芯片映射算法研究
Research on wafer-scale chip mapping task based on genetic algorithm
李成冉 1方佳豪 1尹首一 1魏少军 1胡杨1
作者信息
- 1. 清华大学集成电路学院,北京 100084
- 折叠
摘要
Abstract
In recent years,with the development of artificial intelligence,deep learning has become one of the most important computing loads today.The next generation of artificial intelligence(AI)and high-performance computing applications have put unprecedented demands on the computing power and communication capabilities of computing platforms.Wafer-scale chips integrate ultra-high-density tran-sistors and interconnect communication capabilities on the entire wafer,so it is expected to provide revo-lutionary computing power solutions for future AI and super-computing platforms.Among them,the huge computing resources and unique new architecture of wafer-scale chips pose unprecedented challen-ges to task mapping algorithms.Related research has become a major focus of academic research in re-cent years.This paper focuses on studying the mapping methods of AI tasks on wafer-scale hardware re-sources.By expressing the AI algorithm as multiple convolutional kernels and considering the computa-tional power characteristics of convolutional kernels,a mapping algorithm for wafer-scale chips is de-signed based on genetic algorithms.The simulation results under a series of mapping tasks verifies the effectiveness of the mapping algorithm and revealed the impact of parameters such as execution time and adapter cost on the cost function.关键词
晶圆级芯片/遗传算法/卷积网络映射/人工智能/通信开销Key words
wafer-scale chip/genetic algorithm/convolutional network mapping/artificial intelli-gence/communication overhead分类
信息技术与安全科学引用本文复制引用
李成冉,方佳豪,尹首一,魏少军,胡杨..基于遗传算法的晶圆级芯片映射算法研究[J].计算机工程与科学,2024,46(6):993-1000,8.
