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Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale., , , , , , , , , and 8 other author(s). CoRR, (2021)Deep Learning Recommendation Model for Personalization and Recommendation Systems, , , , , , , , , and 14 other author(s). (2019)cite arxiv:1906.00091Comment: 10 pages, 6 figures.Learning a Dual-Mode Speech Recognition Model VIA Self-Pruning., , , , , and . SLT, page 273-279. IEEE, (2022)TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-Device ASR Models., , , , , , , , , and 4 other author(s). ICASSP, page 10216-10220. IEEE, (2024)Check-N-Run: a Checkpointing System for Training Deep Learning Recommendation Models., , , , , , , and . NSDI, page 929-943. USENIX Association, (2022)Communication Efficient Distributed Training with Distributed Lion., , , , , , , and . CoRR, (2024)FLO Physical Layer: An Overview., , , , , , and . TBC, 53 (1): 145-160 (2007)Binary and Ternary Natural Language Generation., , , , and . ACL (1), page 65-77. Association for Computational Linguistics, (2023)Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts., , , , , , , , , and 3 other author(s). ACL (Findings), page 10424-10443. Association for Computational Linguistics, (2024)MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases., , , , , , , , , and 2 other author(s). ICML, OpenReview.net, (2024)