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Liu Liu has been with the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI) as an assistant professor since July 2022. He has a Ph.D. in Computer Science at University of California, Santa Barbara. His research interests reside in the intersection between computer architecture and machine learning, towards high-performance, energy-efficient, and robust machine intelligence. He leads the research on Elastic Processing & Hardware Architectures, with publications in top-tier conferences on machine learning and computer architecture (e.g., ICML, ICLR, MICRO, and ASPLOS). He earned an M.S. in Electrical and Computer Engineering from UC Santa Barbara in 2015. He is a recipient of the Peter J Frenkel Fellowship from the Institute of Energy Efficiency at UCSB.

Biography »

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Selected Publications All Publications» Google Scholar

• IEEE TC'22 "Dynamic Sparse Attention for Scalable Transformer Acceleration", Liu Liu*, Zheng Qu*, Zhaodong Chen, Fengbin Tu, Yufei Ding, Yuan Xie. (*co-primary) IEEE Transactions on Computers 2022.
• ASPLOS'22 "DOTA: Detect and Omit Weak Attentions for Scalable Transformer Acceleration", Zheng Qu*, Liu Liu*, Fengbin Tu, Zhaodong Chen, Yufei Ding, Yuan Xie. (*co-primary) In 2022 27th International Conference on Architectural Support for Programming Languages and Operating Systems. (Acceptance rate: 20%, 80/397)
• MICRO'21 "ENMC: Extreme Near-Memory Classification via Approximate Screening", Liu Liu*, Jilan Lin*, Zheng Qu, Yufei Ding, Yuan Xie. (*co-primary) In 2021 54th IEEE/ACM International Symposium on Microarchitecture. (Acceptance rate: 22%, 94/423)
• IEEE Computer'21 "π-RT: A Runtime Framework to Enable Energy-Efficient Real-Time Robotic Vision Applications on Heterogeneous Architectures", Liu Liu, Jie Tang, Shaoshan Liu, Bo Yu, Yuan Xie, Jean-Luc Gaudiot. IEEE Computer 54, no. 4 (2021): 14-25.
• MICRO'20 "DUET: Boosting Deep Neural Network Efficiency on Dual-Module Architecture", Liu Liu, Zheng Qu, Lei Deng, Fengbin Tu, Shuangchen Li, Xing Hu, Zhenyu Gu, Yufei Ding, Yuan Xie. In 2020 53rd IEEE/ACM International Symposium on Microarchitecture. (Acceptance rate: 19%, 82/424)
• ICML'20 "Boosting Deep Neural Network Efficiency with Dual-Module Inference", Liu Liu, Lei Deng, Zhaodong Chen, Yuke Wang, Shuangchen Li, Jingwei Zhang, Yihua Yang, Zhenyu Gu, Yufei Ding, Yuan Xie. In 2020 37th International Conference on Machine Learning. (Acceptance rate: 22%, 1088/4990)
• ICLR'19 "Dynamic Sparse Graph for Efficient Deep Learning", Liu Liu*, Lei Deng*, Xing Hu, Maohua Zhu, Guoqi Li, Yufei Ding, Yuan Xie. (*co-primary) In 2019 Seventh International Conference on Learning Representations. (Acceptance rate: 31%, 500/1591)

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Invited Talks

"Elastic Algorithm-Architecture Co-Design for Scalable Machine Learning"

• 11/2022: EBESS Industry Workshop, RPI

"Transformer Acceleration with Dynamic Sparse Attention"

• 10/2022: Invited academic poster at IBM AI Hardware Forum, Yorktown Heights, NY

"Elastic Processing and Hardware Architectures for Machine Learning"

• 01/2022: Seminar at North Carolina State University.
• 01/2022: Seminar at Iowa State University.
• 12/2021: Seminar at Rensselaer Polytechnic Institute.
• 11/2021: Seminar at Binghamton University.

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Awards and Honors

• Grand Finalist, Student Research Competition (SRC) at MICRO 2021
• Peter J Frenkel Fellowship, The Institute for Energy Efficiency at UCSB, 2020
• Computer Science Fellowship, UCSB, 2016

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Teaching

• ECSE 4770 Computer Hardware Design, Instructor, RPI
• CS 16 Problem Solving with Computers I, Teaching Assistant, UCSB
• CS 130A Data Structures and Algorithms I, Teaching Assistant, UCSB

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Professional Service

Conference Reviewer

• NeurIPS 2022
• AICAS 2022
• ICML 2022
• ISCA 2022: External Review Committee
• NeurIPS 2021
• DAC 2020, 2019: External (Expert)

Journal Reviewer

• IEEE Computer Architecture Letters
• Journal of Systems Architecture
• Microprocessors and Microsystems