FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

13 January 2021

Papers citing "FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference"

6 / 6 papers shown

Title
Applying Deep Learning to Ads Conversion Prediction in Last Mile Delivery Marketplace Di Li Xiaochang Miao Huiyu Song Chao Chu Hao Xu Mandar Rahurkar 28 0 0 14 Feb 2025
HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs Chengming Zhang Shaden Smith Baixi Sun Jiannan Tian Jon Soifer Xiaodong Yu S. Song Yuxiong He Dingwen Tao 16 0 0 14 Apr 2023
A Practical Stereo Depth System for Smart Glasses Jialiang Wang D. Scharstein Akash Bapat Kevin Blackburn-Matzen Matthew Yu ... Jan-Michael Frahm Zijian He Peter Vajda Michael F. Cohen M. Uyttendaele MDE 17 5 0 19 Nov 2022
DreamShard: Generalizable Embedding Table Placement for Recommender Systems Daochen Zha Louis Feng Qiaoyu Tan Zirui Liu Kwei-Herng Lai Bhargav Bhushanam Yuandong Tian A. Kejariwal Xia Hu LMTD OffRL 15 28 0 05 Oct 2022
Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks Rakshith Sathish S. Khare Debdoot Sheet 24 4 0 30 Sep 2022
Supporting Massive DLRM Inference Through Software Defined Memory E. K. Ardestani Changkyu Kim Seung Jae Lee Luoshang Pan Valmiki Rampersad ... Krishnakumar Nair Maxim Naumov Christopher Peterson M. Smelyanskiy Vijay Rao BDL 19 20 0 21 Oct 2021