首页> 外文会议>High-Capacity Optical Networks and Enabling Technologies (HONET), 2009 >Performance comparison of case retrieval between Case Based Reasoning and Neural Networks in Predictive Prefetching
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Performance comparison of case retrieval between Case Based Reasoning and Neural Networks in Predictive Prefetching

机译:基于案例的推理与神经网络在预测预取中案例检索的性能比较

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Cache being a fastest medium in memory hierarchy has a vital role to play in memory hierarchy but cannot comprehend speed disparity of processor and memory alone. Predictive prefetching being one of the major concerns in computing systems. The higher level of predictive accuracy is greatly desired. In order to improve the predictability we are looking forward to benefit hybrid of case based reasoning and neural networks. But the most important aspect in this hybrid approach is that of case retrieval which yields related solutions to current problem. We have shown and proved that neural networks have better predictive performance than CBR while performing case retrieval.
机译:高速缓存是内存层次结构中最快的介质,它在内存层次结构中起着至关重要的作用,但不能理解处理器和内存本身的速度差异。预测性预取是计算系统中的主要问题之一。迫切需要更高水平的预测准确性。为了提高可预测性,我们期待受益于基于案例的推理和神经网络的混合。但是,这种混合方法中最重要的方面是案例检索,它为当前问题提供了相关的解决方案。我们已经证明并证明,在执行案例检索时,神经网络比CBR具有更好的预测性能。

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