RAOP: Recurrent Neural Network Augmented Offset Prefetcher
RAOP: Recurrent Neural Network Augmented Offset Prefetcher
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DOI：
10.1145/3422575.3422807
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发表时间：
2020-09
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影响因子：
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通讯作者：
Pengmiao Zhang;Ajitesh Srivastava;Benjamin Brooks;R. Kannan;V. Prasanna
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作者：
Pengmiao Zhang;Ajitesh Srivastava;Benjamin Brooks;R. Kannan;V. Prasanna
大数据的快速发展加上摩尔定律的放缓，使记忆性能成为von Neumann架构中的瓶颈专注于内存访问的预测已使用了复发性神经网络（RNN），因此缺乏在预摘要中使用这种预测的框架。论文介绍了RNN增强偏移预摘要（RAOP）框架，该框架由两个部分组成：一个基于RNN的预测指标和一个偏移预取模块，通过利用RNN的临时访问来提高预测访问权限。当前的地址和RNN预测地址。将RNN预测器扩大到预摘模块中的简单下线预摘要的效果会导致3.22倍，4.2倍和15.6％的预购精度，覆盖范围和加速度提高。 RAOP并将其与几个最先进的预摘要进行比较将RNN预测器扩大到BOP，RIOP导致预取精度，覆盖范围和加速度提高了6.5倍，9.2倍和12.8％。
The rapid development of Big Data coupled with slowing down of Moore’s law has made the memory performance a bottleneck in the von Neumann architecture. Machine learning has the potential to provide opportunities to address the memory performance issues, specifically through data access prediction. While recent works focusing on the prediction of memory accesses have used recurrent neural networks (RNN), there is a lack of a framework utilizing such prediction in a prefetcher. This paper introduces the RNN Augmented Offset Prefetcher (RAOP) framework, which consists of two parts: an RNN-based predictor and an offset prefetching module. By leveraging the RNN predicted access as a temporal reference, RAOP improves prefetching performance by executing offset prefetching for both the current address and the RNN predicted address. We implement the RNN in the predictor with a compressed long short-term memory (LSTM) model and demonstrate the effect of augmenting an RNN predictor to a simple next-line prefetcher in the prefetching module results in 3.22x, 4.2x, and 15.6% improvement in prefetch accuracy, coverage, and speedup. We further implement a best-offset prefetcher (BOP) in RAOP and compare it to several state-of-the-art prefetchers. Results show that RAOP achieves a mean 4.05% speedup by prefetching in last level cache, outperforming state-of-the-art prefetchers. By augmenting an RNN predictor to BOP, RAOP results in 6.5x, 9.2x, and 12.8% improvement in prefetch accuracy, coverage, and speedup.