Perceptron Learning for Reuse Prediction
Perceptron Learning for Reuse Prediction 論文的主要貢獻： 通過結合多種特徵作爲輸入，感知器學習可以大大的改善 cache block reuse預測 基於感知器的預測結果可以用於幫助cache的replacement和bypass優化 基於感知器學習的cache管理策略會比更大一倍的基於LRU的cache 更高效 感知器學習在微體系結構中的應用： 最早的感
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相關文章
- 1. Octave implementation for Programming Assignment 1: The perceptron learning algorithm.
- 2. Automatically Learning Semantic Features for Defect Prediction
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- 6. Learning Informative Edge Maps for Indoor Scene Layout Prediction
- 7. Model-Reuse Attacks on Deep Learning Systems
- 8. Java Design Patterns for Reuse
- 9. Perceptron Learning Algorithm(python實現)
- 10. [Reinforcement Learning] Model-Free Prediction