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Scaling Local Self-Attention for Parameter Efficient Visual Backbones.

, , , , , and . CVPR, page 12894-12904. Computer Vision Foundation / IEEE, (2021)

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Exploring memory consistency for massively-threaded throughput-oriented processors., and . ISCA, page 201-212. ACM, (2013)A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers., , , , , , , , , and 7 other author(s). PACT, page 1-16. IEEE, (2021)GSPMD: General and Scalable Parallelization for ML Computation Graphs., , , , , , , , , and 6 other author(s). CoRR, (2021)Exploring the limits of Concurrency in ML Training on Google TPUs., , , , , , , , , and 9 other author(s). CoRR, (2020)Exploiting Parallelism in GPUs.. Duke University, Durham, NC, USA, (2014)base-search.net (ftdukeunivdsp:oai:dukespace.lib.duke.edu:10161/8710).Large-Scale Discrete Fourier Transform on TPUs., , , , and . CoRR, (2020)Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training., , , , , and . CoRR, (2020)QuickRelease: A throughput-oriented approach to release consistency on GPUs., , , , , , , and . HPCA, page 189-200. IEEE Computer Society, (2014)Unified Scaling Laws for Routed Language Models., , , , , , , , , and 16 other author(s). ICML, volume 162 of Proceedings of Machine Learning Research, page 4057-4086. PMLR, (2022)Heterogeneous-race-free memory models., , , , , , and . ASPLOS, page 427-440. ACM, (2014)