期刊文献+

一种改进的神经网络分支预测技术 被引量:4

An Improved Branch Prediction Based on the Neural Network
下载PDF
导出
摘要 通过研究神经网络的算法特征,提出一种改进方法,即设置一个门限值η,减小该算法的运算量以减小其访问延迟,提高该算法的实用性.研究表明该方法能够实现较快的访问速度. By studying characteristics of neural network algorithm ,this paper proposes an improved method ,which sets a threshold value η to reduce the amount of calculation for reducing its access latency and improve the practicality of the algorithm .Studies show that this method can achieve faster access .
出处 《微电子学与计算机》 CSCD 北大核心 2014年第11期152-155,共4页 Microelectronics & Computer
关键词 访问延时 分支预测 神经网络预测 access latency branch prediction neural prediction
  • 相关文献

参考文献3

  • 1Daniel A. Jiménez.Generalizing neural branch prediction[J].ACM Transactions on Architecture and Code Optimization (TACO).2009(4)
  • 2Gabriel H. Loh,Daniel A. Jiménez.Modulo Path History for the Reduction of Pipeline Overheads in Path-based Neural Branch Predictors[J].International Journal of Parallel Programming.2008(2)
  • 3Daniel A. Jiménez,Calvin Lin.Neural methods for dynamic branch prediction[J].ACM Transactions on Computer Systems (TOCS).2002(4)

同被引文献46

  • 1严薇,龙昭乾,刘亮晴.基于人工神经网络的住宅造价估算[J].建筑经济,2009,30(S1):312-315. 被引量:5
  • 2赵朝君,陈晨,陈志坚,孟建熠.基于历史长度自适应的分支预测方法[J].计算机辅助设计与图形学学报,2015,27(4):764-770. 被引量:2
  • 3KAYACAN E,ULUTAS B,KAYNAK O.Grey system theory-based models in time series prediction[J].Expert Systems with Applications,2010,37(2):1784-1789.
  • 4YIN M S.Fifteen years of grey system theory research:A historical review and bibliometric analysis[J].Expert Systems with Applications,2013,40(7):2767-2775.
  • 5VAPNIK V N.Statistical Learning Theory[M].New York:John Wiley,1998:34-42.
  • 6ALDRICH C,AURET L.Statistical learning theory and kernel-based methods[C] // Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods.London:Springer ,2013:117-181.
  • 7PENG X.TSVR:An efficient twin support vector machine for regression[J].Neural Networks,2010,23(3):365-372.
  • 8SUYKENS J A K,VANDEWALLE J.Least squares support vector machine classifiers[J].Neural Processing Letter,1999(3):293-300.
  • 9ABDI H,WILLIAMS L J.Principal component analysis[J].Wiley Interdisciplinary Reviews:Computational Statistics,2010,2(4):433-459.
  • 10CRISTIANINI N,SHAWE-TAYLOR J.An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods [M].Cambridge:Cambridge University Press,2000:30-34.

引证文献4

二级引证文献23

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部