Maxim Naumov joined Facebook in January 2018. His interests include deep learning, parallel algorithms and numerical methods. In the past, he held different positions at Nvidia Research, Emerging Applications and Platform teams. He has also worked at Intel Corporation Microprocessor Technology and Computational Software Labs. Maxim received his PhD in computer science (with specialization in computational science and engineering) in 2009 and BS in computer science and mathematics in 2003 from Purdue University – West Lafayette. He was awarded the 2008-09 Intel Foundation PhD Fellowship during his graduate studies.
Interests
Deep learning, parallel algorithms, numerical methods, high performance computing
Related Links
Latest Publications
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
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
arXiv - September 3, 2020
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
Liu Ke, Udit Gupta, Benjamin Youngjae Cho, David Brooks, Vikas Chandra, Utku Diril, Amin Firoozshahian, Kim Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Meng Li, Bert Maher, Dheevatsa Mudigere, Maxim Naumov, Martin Schatz, Mikhail Smelyanskiy, Xiaodong Wang, Brandon Reagen, Carole-Jean Wu, Mark Hempstead, Xuan Zhang
ISCA - May 22, 2020
The Architectural Implications of Facebook’s DNN-based Personalized Recommendation
Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Mark Hempstead, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang
HPCA - February 17, 2020
Bandana: Using Non-Volatile Memory for Storing Deep Learning Models
Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim Hazelwood, Asaf Cidon, Sachin Katti
SysML - April 2, 2019
On the Dimensionality of Embeddings for Sparse Features and Data
Maxim Naumov
ArXiv - January 7, 2019
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Khudia, James Law, Parth Malani, Andrey Malevich, Satish Nadathur, Juan Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy
ArXiv - November 24, 2018
On Periodic Functions as Regularizers for Quantization of Neural Networks
Maxim Naumov, Utku Diril, Jongsoo Park, Benjamin Ray, Jedrzej Jablonski, Andrew Tulloch
ArXiv - November 24, 2018