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基于半监督集成学习的多核设计空间探索

李丹丹 姚淑珍 王颖 王森章 谭火彬

李丹丹, 姚淑珍, 王颖, 等 . 基于半监督集成学习的多核设计空间探索[J]. 北京航空航天大学学报, 2018, 44(4): 792-801. doi: 10.13700/j.bh.1001-5965.2017.0297
引用本文: 李丹丹, 姚淑珍, 王颖, 等 . 基于半监督集成学习的多核设计空间探索[J]. 北京航空航天大学学报, 2018, 44(4): 792-801. doi: 10.13700/j.bh.1001-5965.2017.0297
LI Dandan, YAO Shuzhen, WANG Ying, et al. Multicore design space exploration via semi-supervised ensemble learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 792-801. doi: 10.13700/j.bh.1001-5965.2017.0297(in Chinese)
Citation: LI Dandan, YAO Shuzhen, WANG Ying, et al. Multicore design space exploration via semi-supervised ensemble learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 792-801. doi: 10.13700/j.bh.1001-5965.2017.0297(in Chinese)

基于半监督集成学习的多核设计空间探索

doi: 10.13700/j.bh.1001-5965.2017.0297
基金项目: 

航空科学基金 2013ZC51023

详细信息
    作者简介:

    李丹丹  女, 博士研究生。主要研究方向:处理器设计、机器学习、软件工程技术

    姚淑珍  女, 博士, 教授, 博士生导师。主要研究方向:先进软件工程技术、形式化方法、Petri网理论

    王颖  男, 博士, 讲师。主要研究方向:存储系统、节能加速器、容错体系结构

    王森章  男, 博士, 副研究员。主要研究方向:数据挖掘、社交网络分析、大数据

    谭火彬  男, 博士, 讲师。主要研究方向:软件工程、软件建模

    通讯作者:

    姚淑珍, E-mail: szyao@buaa.edu.cn

  • 中图分类号: TP302

Multicore design space exploration via semi-supervised ensemble learning

Funds: 

Aeronautical Science Foundation of China 2013ZC51023

More Information
  • 摘要:

    随着处理器的系统结构日趋复杂,设计空间呈指数式增长,并且软件模拟技术极为费时,成为处理器设计的重要挑战。提出了一种结合集成学习和半监督学习技术的高效设计空间探索方法。具体而言,该方法包括2个阶段:使用均匀随机采样方法从处理器设计空间中选择一小组具有代表性的设计点,通过模拟获得性能响应,从而组成训练数据集;提出基于半监督学习的AdaBoost(SSLBoost)模型预测未模拟的样本配置的响应,从而搜索最优的处理器设计配置。实验结果表明,与现有的基于人工神经网络和支持向量机(SVM)的有监督预测模型相比,SSLBoost模型能够使用更少的模拟样本构建出不差于现有方法性能的预测模型;而当模拟样本数量相同时,SSLBoost模型的预测精度更高。

     

  • 图 1  基于半监督集成学习的设计空间探索框架

    Figure 1.  Design space exploration framework based on semi-supervised ensemble learning

    图 2  基于半监督学习的AdaBoost模型流程图

    Figure 2.  Flowchart of AdaBoost model based on semi-supervised learning

    图 3  在多核设计场景下不同方法的预测精度对比

    Figure 3.  Prediction accuracy comparsion of different methods in multicore design scenarios

    图 4  多核设计场景下SSLBoost模型关于不同数量的训练迭代次数的预测精度

    Figure 4.  Prediction accuracy of SSLBoost model with respect to different numbers of training iterations in multicore design scenarios

    表  1  多核设计空间

    Table  1.   Multicore design space

    设计参数 设计参数取值 数量
    Band Width/(GB·s-1) 8~64:8+ 8
    Frequency/GHz 1~4.5: 0.5+ 8
    Issue Width 1, 2, 4, 8 4
    Number of Cores 1, 2, 4, 8 4
    L2 Cache Size/MB 2, 4, 8, 16 4
    L2 Cache Block Size/B 16, 32, 64, 128 4
    L2 Cache Associativity 2, 4, 8, 16 4
    L2 Cache MSHR 32~256:2* 4
    L1 Dcache/KB 16, 32, 64, 128 4
    L1 ICache/KB 16, 32, 64, 128 4
    下载: 导出CSV

    表  2  为达到SSLBoost模型相同的预测精度,ANN和SVM所需要的模拟配置(训练样本)数量

    Table  2.   Numbers of simulated configurations (training examples) required by ANN and SVM to achieve the same level of prediction accuracy as SSLBoost model

    基准程序 配置数量
    ANN SVM
    blackscholes 360 500+
    bodytrack 350 300
    canneal 320 500+
    dedup 370 270
    facesim 440 500+
    ferret 310 480
    fluidanimate 430 500+
    freqmine 470 500+
    streamcluster 400 500+
    vips 370 500+
    平均 382 455+
    下载: 导出CSV

    表  3  各种模型预测的最优配置对比(vips)

    Table  3.   Predicted optimal configurations of different models(vips)

    设计参数及性能 SSLBoost模型 ANN SVM
    Band Width/(GB·s-1) 64 8 40
    Frequency/GHz 4.5 4.5 4
    Issue/Fetch/Commit Width 4 2 4
    Number of Cores 1 1 2
    L2 Cache Size/MB 4 2 2
    L2 Cache Block Size/B 64 128 64
    L2 Cache Associativity 8 2 2
    L2 Cache MSHR 128 64 128
    L1 Dcache/KB 128 128 64
    L1 ICache/KB 64 16 16
    真实模拟性能/ms 11.52 11.78 13.68
    预测性能/ms 11.12 10.27 10.42
    预测误差/% 3.5 12.9 23.8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-05-11
  • 录用日期:  2017-06-16
  • 刊出日期:  2018-04-20

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