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电子学报  2012 

基于神经网络的重构指令预取机制及其可扩展架构

DOI: 10.3969/j.issn.0372-2112.2012.07.031, PP. 1476-1480

Keywords: 可重构处理器,配置信息预取,改进神经网络算法,可扩展存储架构

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Abstract:

针对动态可重构处理器的配置信息加载延时,提出了一种基于神经网络的可扩展的重构指令预取机制.增加感受器的历史指令信息,并结合感受器权重构建新型的感受器模型,通过权重与历史指令信息的协同训练学习重构指令调用规律.在处理器运行过程中,提前完成对后续重构指令的预测及配置信息的预取,隐藏指令重构成本.进一步提出了本方法的可扩展实现框架,神经网络的学习结果作为重构指令的关联信息,被移至内存并分布式存储.在重构指令预取时,完成对神经网络学习信息的加载.实验结果表明,该方法对重构指令的预测准确率达91%,综合性能平均提升40%.

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