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Development of a wireless sensor network for animal management: Experiences with Moosense., , , , , and . IEEE ANTS, page 1-6. IEEE, (2014)Adaptive Transmission Power Protocol for heterogeneous Wireless Sensor Networks., , and . NCC, page 1-5. IEEE, (2015)Deep Learning Recommendation Model for Personalization and Recommendation Systems, , , , , , , , , and 14 other author(s). (2019)cite arxiv:1906.00091Comment: 10 pages, 6 figures.Supporting Massive DLRM Inference through Software Defined Memory., , , , , , , , , and 10 other author(s). ICDCS, page 302-312. IEEE, (2022)Software-hardware co-design for fast and scalable training of deep learning recommendation models., , , , , , , , , and 43 other author(s). ISCA, page 993-1011. ACM, (2022)Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications., , , , , , , , , and 18 other author(s). CoRR, (2018)Pratham: A Low-Cost Wireless Node with Programmable Radio Range and Over-the-Air Programming Capability for Resource-Constrained Applications., , and . ISED, page 49-53. IEEE Computer Society, (2014)VCX: An industry initiative to create an objective camera module evaluation for mobile devices., , , , , and . Photography, Mobile, and Immersive Imaging, page 1-5. Society for Imaging Science and Technology, (2018)MTIA: First Generation Silicon Targeting Meta's Recommendation Systems., , , , , , , , , and 46 other author(s). ISCA, page 80:1-80:13. ACM, (2023)Optimal task scheduling policy in energy harvesting wireless sensor networks., , and . WCNC, page 1030-1035. IEEE, (2015)