ReSemble: Reinforced Ensemble Framework for Data Prefetching
Author:
Affiliation:
1. University of Southern California,Los Angeles,USA
2. United States Army Research Laboratory,Los Angeles,USA
3. Intel Corporation,Bangalore,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10046045/10045783/10046113.pdf?arnumber=10046113
Reference59 articles.
1. Temporal Prefetching Without the Off-Chip Metadata
2. Attention is all you need;vaswani;Advances in neural information processing systems,2017
3. Practical off-chip meta-data for temporal memory streaming
4. LSTM: A Search Space Odyssey
5. Predicting memory accesses
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1. Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching;2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2024-05-27
2. CHROME: Concurrency-Aware Holistic Cache Management Framework with Online Reinforcement Learning;2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA);2024-03-02
3. RL-CoPref: a reinforcement learning-based coordinated prefetching controller for multiple prefetchers;The Journal of Supercomputing;2024-02-27
4. Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach;2023 IEEE International Conference on Cluster Computing (CLUSTER);2023-10-31
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