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Archit. Code Optim."],"published-print":{"date-parts":[[2019,12,31]]},"abstract":"<jats:p>\n            Memory prefetchers are designed to identify and prefetch specific access patterns, including\n            <jats:italic>spatiotemporal<\/jats:italic>\n            locality (e.g., strides, streams), recurring patterns (e.g., varying strides, temporal correlation), and specific irregular patterns (e.g., pointer chasing, index dereferencing). However, existing prefetchers can only target premeditated patterns and relations they were designed to handle and are unable to capture access patterns in which they do not specialize. In this article, we propose a context-based neural network (NN) prefetcher that dynamically adapts to arbitrary memory access patterns. Leveraging recent advances in machine learning, the proposed NN prefetcher correlates program and machine contextual information with memory accesses patterns, using online-training to identify and dynamically adapt to unique access patterns exhibited by the code. By targeting\n            <jats:italic>semantic locality<\/jats:italic>\n            in this manner, the prefetcher can discern the useful context attributes and learn to predict previously undetected access patterns, even within noisy memory access streams. We further present an architectural implementation of our NN prefetcher, explore its power, energy, and area limitations, and propose several optimizations. We evaluate the neural network prefetcher over SPEC2006, Graph500, and several microbenchmarks and show that the prefetcher can deliver an average speedup of 21.3% for SPEC2006 (up to 2.3\u00d7) and up to 4.4\u00d7 on kernels over a baseline of PC-based stride prefetcher and 30% for SPEC2006 over a baseline with no prefetching.\n          <\/jats:p>","DOI":"10.1145\/3345000","type":"journal-article","created":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T16:35:58Z","timestamp":1571157358000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["A Neural Network Prefetcher for Arbitrary Memory Access Patterns"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4853-061X","authenticated-orcid":false,"given":"Leeor","family":"Peled","sequence":"first","affiliation":[{"name":"Technion-Israel Institute of Technology, Haifa, Israel"}]},{"given":"Uri","family":"Weiser","sequence":"additional","affiliation":[{"name":"Technion-Israel Institute of Technology, Haifa, Israel"}]},{"given":"Yoav","family":"Etsion","sequence":"additional","affiliation":[{"name":"Technion-Israel Institute of Technology, Haifa, Israel"}]}],"member":"320","published-online":{"date-parts":[[2019,10,15]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the International Joint Conference on Neural Networks (IJCNN\u201917)","author":"Alemdar H.","unstructured":"H. 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