Search Results for author: Hector Yuen

Found 6 papers, 0 papers with code

Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale

no code implementations26 May 2021 Zhaoxia, Deng, Jongsoo Park, Ping Tak Peter Tang, Haixin Liu, Jie, Yang, Hector Yuen, Jianyu Huang, Daya Khudia, Xiaohan Wei, Ellie Wen, Dhruv Choudhary, Raghuraman Krishnamoorthi, Carole-Jean Wu, Satish Nadathur, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy

We share in this paper our search strategies to adapt reference recommendation models to low-precision hardware, our optimization of low-precision compute kernels, and the design and development of tool chain so as to maintain our models' accuracy throughout their lifespan during which topic trends and users' interests inevitably evolve.

Recommendation Systems

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems

no code implementations20 Mar 2020 Maxim Naumov, John Kim, Dheevatsa Mudigere, Srinivas Sridharan, Xiaodong Wang, Whitney Zhao, Serhat Yilmaz, Changkyu Kim, Hector Yuen, Mustafa Ozdal, Krishnakumar Nair, Isabel Gao, Bor-Yiing Su, Jiyan Yang, Mikhail Smelyanskiy

Large-scale training is important to ensure high performance and accuracy of machine-learning models.

Distributed, Parallel, and Cluster Computing 68T05, 68M10 H.3.3; I.2.6; C.2.1

Post-Training 4-bit Quantization on Embedding Tables

no code implementations5 Nov 2019 Hui Guan, Andrey Malevich, Jiyan Yang, Jongsoo Park, Hector Yuen

Continuous representations have been widely adopted in recommender systems where a large number of entities are represented using embedding vectors.

Quantization Recommendation Systems

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