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Deep Learning with Limited Numerical Precision.

, , , and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 1737-1746. JMLR.org, (2015)

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Staleness-aware Async-SGD for Distributed Deep Learning., , , and . CoRR, (2015)Accelerator-aware Neural Network Design using AutoML., and . CoRR, (2020)Guest Editors' Introduction: Hardware and Algorithms for On-Chip Learning., , , and . ACM J. Emerg. Technol. Comput. Syst., 13 (3): 30:1-30:3 (2017)Discovering Multi-Hardware Mobile Models via Architecture Search., , , , , , , , , and . CVPR Workshops, page 3022-3031. Computer Vision Foundation / IEEE, (2021)Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study., , and . IJCAI, page 4854-4858. ijcai.org, (2017)Deep Learning with Limited Numerical Precision., , , and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 1737-1746. JMLR.org, (2015)Staleness-Aware Async-SGD for Distributed Deep Learning., , , and . IJCAI, page 2350-2356. IJCAI/AAAI Press, (2016)Model Accuracy and Runtime Tradeoff in Distributed Deep Learning., , and . CoRR, (2015)To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression., and . ICLR (Workshop), OpenReview.net, (2018)MobileDets: Searching for Object Detection Architectures for Mobile Accelerators., , , , , , , , and . CoRR, (2020)