We propose a caching policy that uses a feedforward neural network (FNN) to predict content popularity. Our scheme outperforms popular eviction policies like LRU or ARC, but also a new policy relying on the more complex recurrent neural networks. At the same time, replacing the FNN predictor with a naive linear estimator does not degrade caching performance significantly, questioning then the role of neural networks for these applications.
Papers @cite_3 @cite_12 propose two different LSTM-NNs for prefetching inside a processor. Instead, in our paper, we focus on internet applications like web proxies, or content delivery networks (CDNs), and we consider reactive caching, i.e. contents can be retrieved only once they are requested. Moreover, @cite_3 @cite_12 evaluate the quality of their solutions only in terms of prediction accuracy and not of the final data retrieval time or other system metrics prefetching should improve. In this paper we conclude that the relation between prediction accuracy and final performance of the caching system may be weaker than one would expect.
@cite_12 @cite_3
2792738181 2762076598
The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recent advances in machine learning in software optimizations, augmenting or replacing traditional heuristics and data structures. However, the space of machine learning for computer hardware architecture is only lightly explored. In this paper, we demonstrate the potential of deep learning to address the von Neumann bottleneck of memory performance. We focus on the critical problem of learning memory access patterns, with the goal of constructing accurate and efficient memory prefetchers. We relate contemporary prefetching strategies to n-gram models in natural language processing, and show how recurrent neural networks can serve as a drop-in replacement. On a suite of challenging benchmark datasets, we find that neural networks consistently demonstrate superior performance in terms of precision and recall. This work represents the first step towards practical neural-network based prefetching, and opens a wide range of exciting directions for machine learning in computer architecture research. Hardware prefetching is an efficient mechanism to hide cache miss penalties. Accuracy, coverage, and timeliness are three primary metrics in evaluating prefetcher performance. Highly accurate hardware prefetches are desired to predict complex memory access patterns in multicore systems. In this paper, we propose a long short term memory (LSTM) prefetcher---a neural network based hardware prefetcher. Offline experiment shows that the proposed LSTM prefetcher achieves higher accuracy and better coverage on a set of evaluated traces.