Liu Ke

About me

I am a PhD student in Washington University in St Louis advised by Prof. Xuan Zhang. My research focuses on optimizing large-scale deep learning-based personalized recommendation systems. To enable efficient production deployment, my work improves the recommendation serving at-scale through the practical near-memory processing solution, the heterogeneity-aware datacenter resource management, and our vision of designing the next-generation datacenter in a disaggregated manner for future fast-evolving recommendation models.




  • Washington University in St. Louis

Ph.D. in Electrical Engineering (2018-Present)

M.S. in Electrical Engineering (2016-2018)

Advisor: Prof. Xuan Zhang

  • Sun Yat-Sen University

B.E. in Software Engineering (2012-2016)

Professional Experience

  • Meta / Facebook

Mentor: Dr. Hsien-Hsin Sean Lee

Facebook AI Research (FAIR) Visiting Researcher (Jan. 2020 - Aug. 2022)

AI Infrastructure Research Intern (May 2019 - Jan. 2020)

Selected Publications

For a complete and up to date list of publications please visit Google Scholar.

  • DisaggRec: Architecting Disaggregated Systems for Large-Scale Personalized Recommendation. Liu Ke, Xuan Zhang, Benjamin Lee, G. Edward Suh, Hsien-Hsin S. Lee. Under Submission, [ArXiv]

  • MeNDA: a Near-Memory Multi-Way Merge Solution for Sparse Transposition and Dataflows. Siying Feng, Xin He, Kuan-Yu Chen, Liu Ke, Xuan Zhang, David Blaauw, Trevor Mudge, Ronald Dreslinski. International Symposium on Computer Architecture (ISCA), 2022

  • Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation, Liu Ke, Udit Gupta, Mark Hempstead, Carole-Jean Wu, Hsien-Hsin S. Lee, Xuan Zhang. IEEE International Symposium on High-Performance Computer Architecture (HPCA 2022) [PDF]

  • SecNDP: Secure Near-Data Processing with Untrusted Memory, Wenjie Xiong*, Liu Ke*, Dimitrije Jankov, Michael Kounavis, Xiaochen Wang, Eric Northup, Jie Amy Yang, Bilge Acun, Carole-Jean Wu, Ping Tak Peter Tang, G. Edward Suh, Xuan Zhang, Hsien-Hsin S. Lee, (* Equal Contribution). IEEE International Symposium on High-Performance Computer Architecture (HPCA 2022)

  • Near-Memory Processing in Action: Accelerating Personalized Recommendation with AxDIMM, Liu Ke, Xuan Zhang, Jinin So, Jong-Geon Lee, Shin-Haeng Kang, Sukhan Lee, Songyi Han, Yeongon Cho, Jin Hyun Kim, Yongsuk Kwon, Kyungsoo Kim, Jin Jung, Ilkwon Yun, Sung Joo Park, Hyunsun Park, Joonho Song, Jeonghyeon Cho, Kyomin Sohn, Nam Sung Kim, Hsien-Hsin Sean Lee. IEEE Micro 2022 [PDF]

  • RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. Liu Ke, Udit Gupta, Carole-Jean Wu, Benjamin Youngjae Cho, Mark Hempstead, Brandon Reagen, Xuan Zhang, 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. International Symposium on Computer Architecture (ISCA 2020) [PDF][Slides]

  • NNest: Early-stage design space exploration tool for neural network inference accelerators. Liu Ke, Xin He, Xuan Zhang. International Symposium on Low Power Electronics and Design (ISLPED 2018)

  • AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference. Xin He, Liu Ke, Wenyan Lu, Guihai Yan, Xuan Zhang. International Symposium on Low Power Electronics and Design (ISLPED), 2018.


  • Computer Memory Module Processing Device with Cache Storage, Liu Ke, Xuan Zhang, Udit Gupta, Carole-Jean Wu, Mark David Hempstead, Brandon Reagen, Hsien-Hsin Sean Lee, US Patent 11442866, 2022