Accurate principal component analysis via a few iterations of alternating least squares. - Arthur Szlam; Andrew Tulloch; Mark Tygert (CoRR, 2016)

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. - Priya Goyal; Piotr Dollár; Ross B. Girshick; Pieter Noordhuis; Lukasz Wesolowski; Aapo Kyrola; Andrew Tulloch; Yangqing Jia; Kaiming He (CoRR, 2017)

High performance ultra-low-precision convolutions on mobile devices. - Andrew Tulloch; Yangqing Jia (CoRR, 2017)

Accurate Low-Rank Approximations Via a Few Iterations of Alternating Least Squares. - Arthur Szlam; Andrew Tulloch; Mark Tygert (SIAM J. Matrix Anal. Appl., 2017)

On Periodic Functions as Regularizers for Quantization of Neural Networks. - Maxim Naumov; Utku Diril; Jongsoo Park; Benjamin Ray; Jedrzej Jablonski; Andrew Tulloch (CoRR, 2018)

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications. - Jongsoo Park; Maxim Naumov; Protonu Basu; Summer Deng; Aravind Kalaiah; Daya Shanker Khudia; James Law; Parth Malani; Andrey Malevich; Nadathur Satish; Juan Miguel Pino; Martin Schatz; et al. (CoRR, 2018)

Machine Learning at Facebook: Understanding Inference at the Edge. - Carole-Jean Wu; David Brooks 001; Kevin Chen; Douglas Chen; Sy Choudhury; Marat Dukhan; Kim M. Hazelwood; Eldad Isaac; Yangqing Jia; Bill Jia; Tommer Leyvand; Hao Lu; et al. (HPCA, 2019)

Hybrid Composition with IdleBlock: More Efficient Networks for Image Recognition. - Bing Xu; Andrew Tulloch; Yunpeng Chen; Xiaomeng Yang; Lin Qiao (CoRR, 2019)

Mixed-Precision Embedding Using a Cache. - Jie Amy Yang; Jianyu Huang; Jongsoo Park; Ping Tak Peter Tang; Andrew Tulloch (CoRR, 2020)

High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. - Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Andrew Tulloch; Srinivas Sridharan 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; Leon Gao; et al. (CoRR, 2021)

Software-hardware co-design for fast and scalable training of deep learning recommendation models. - Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Zhihao Jia; Andrew Tulloch; Srinivas Sridharan 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; et al. (ISCA, 2022)

MTrainS: Improving DLRM training efficiency using heterogeneous memories. - Hiwot Tadese Kassa; Paul Johnson; Jason Akers; Mrinmoy Ghosh; Andrew Tulloch; Dheevatsa Mudigere; Jongsoo Park; Xing Liu; Ronald G. Dreslinski; Ehsan K. Ardestani (CoRR, 2023)

GPT-4o System Card. - Aaron Hurst; Adam Lerer; Adam P. Goucher; Adam Perelman; Aditya Ramesh; Aidan Clark; AJ Ostrow; Akila Welihinda; Alan Hayes; Alec Radford; Aleksander Madry; Alex Baker-Whitcomb; et al. (CoRR, 2024)