基于演化计算的处理器结构优化关键技术研究

项目名称： 基于演化计算的处理器结构优化关键技术研究

项目编号： No.61473275

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 陈天石

作者单位： 中国科学院计算技术研究所

项目金额： 83万元

中文摘要： 处理器研发中的首要问题是确定合适的处理器体系结构，优化处理器的性能/功耗（以下简称处理器结构优化）。在工业界传统的研发流程中，处理器结构由资深研发人员通过极为缓慢的处理器模拟并结合个人经验来确定。为提高结构优化的效率和可靠性，近年来许多研究者将机器学习方法应用于处理器性能/功耗建模。这类方法对提高处理器研发的效率有很大帮助，但还不能适应处理器异构化、低功耗和可扩展三大新挑战。 本项目面向这三大挑战，研究基于演化计算的处理器结构优化方法。项目组借鉴高维演化优化、演化博弈等演化计算领域的经典学术思想，提出异构多核结构的演化优化、基于演化博弈的低功耗结构、可扩展互连结构的编码及优化等一系列关键技术，力争实现一个处理器结构优化的开源平台，服务于国产处理器的研发。

中文关键词： 演化计算；处理器；高维优化；演化博弈

英文摘要： An important issue in the design flow of a processor is to decide an appropriate processor architecture which optimizes the performance/power of the processor (architecture optimization for short). In the traditional industrial flow, the processor architecture is decided by senior researchers according to their personal experiences as well as extremely-slow processor simulation. In order to enhance the efficiency and reliability of architecture optimization, recently there have been a few studies which use machine learning techniques to model processor performance/power. While helpful in enhancing the efficiency of designing processor, such techniques fail to adapt to three emerging challenges, namely, the heterogeneity challenge, the low-power challenge, and the scalability challenge. Targeting at the three challenges, this project studies a novel methodology of architecture optimization based on evolutionary computation. Inspired by classical paradigms in evolutionary computation, such as large-scale evolutionary optimization and evolutionary game theory, this project proposes a series of key techniques, including evolutionary optimization of heterogeneous multicore architecture, low-power architecture based on evolutionary game theory, and encoding and optimization of scalable interconnection architecture. The ultimate goal is to implement an open-source architecture optimization platform for the research and development of China's domestic processors.

英文关键词： Evolutionary Computation;Processor;Large-scale Optimization;Evolutionary Game