People

Kim Hazelwood

Senior Engineering Manager

I work at the interface between systems and ML, and lead the Facebook AI Infrastructure Foundation, which focuses on efficient and scalable hardware and software platforms for Facebook’s production and mobile use cases of machine learning. My research interests include workload characterization, performance analysis, computer systems architectures and scalable datacenter systems. Prior to Facebook, I held positions including a tenured Associate Professor at the University of Virginia, Software Engineer at Google, and Director of Systems Research at Yahoo Labs. I received a PhD in computer science from Harvard University in 2004, and am the recipient of an NSF CAREER Award, the Anita Borg Early Career Award, the MIT Technology Review Top 35 Innovators under 35 Award and the ACM SIGPLAN 10-Year Test of Time Award. I have authored over 50 conference papers and one book.

Interests

AI/ML platforms, workload characterization, HW/SW co-design, computer systems architectures and performance analysis and tools

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

Machine Learning at Facebook: Understanding Inference at the Edge

Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim Hazelwood, Eldad Isaac, Yangqing Jia, Bill Jia, Tommer Leyvand, Hao Lu, Yang Lu, Lin Qiao, Brandon Reagen, Joe Spisak, Fei Sun, Andrew Tulloch, Peter Vajda, Xiaodong Wang, Yanghan Wang, Bram Wasti, Yiming Wu, Ran Xian, Sungjoo Yoo, Peizhao Zhang

HPCA 2019 - February 16, 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

Reducing DRAM Footprint with NVM in Facebook

Assaf Eisenman, Darryl Gardner, Islam AbdelRahman, Jens Axboe, Siying Dong, Kim Hazelwood, Chris Petersen, Asaf Cidon, Sachin Katti

EUROSYS 2018 - April 23, 2018

Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

Kim Hazelwood, Sarah Bird, David Brooks, Soumith Chintala, Utku Diril, Dmytro Dzhulgakov, Mohamed Fawzy, Bill Jia, Yangqing Jia, Aditya Kalro, James Law, Kevin Lee, Jason Lu, Pieter Noordhuis, Misha Smelyanskiy, Liang Xiong, Xiaodong Wang

HPCA 2018 - February 24, 2018

Facebook AI Research

Facebook contributes to MLPerf, open-sources Mask R-CNN2Go

December 13, 2018