Jeff Zhang
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ISTB4 467 Arizona State University Tempe, AZ 85287
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Mail code: 5706Campus: Tempe
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Jeff (Jun) Zhang is an assistant professor in the School of Electrical, Computer and Energy Engineering at ASU. He received his PhD degree from New York University. From 2020-2022, Zhang was a postdoctoral fellow at Harvard University. Zhang’s general research interests are in deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. He received best paper award nominations at DATE 2022 and VTS 2018, and best presentation award nomination at DATE Ph.D. Forum 2020. Jeff serves on the technical program committee of several top conferences in the area of computer engineering and computer hardware, and has served as a reviewer for several IEEE and ACM journals.
Awards and Honors
- Best Paper Award Candidate, IEEE DATE, 2022
- Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020
- Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018
- Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016
Experience
Arizona State University, Tempe AZ
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Assistant Professor Jan. 2023 to now
Harvard University, Cambridge MA
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Research Associate Jan. 2023 to now
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Postdoctoral Fellow Aug. 2020 to Dec. 2022
- Teaching Fellow
- - Topics in Mixed Signal Integrated Circuits. (Fall'22, Spring'21)
- - Design of VLSI Circuits and Systems. (Spring'22)
New York University, New York NY
- Research Assistant Sep. 2015 to Aug. 2020
- Teaching Assistant
- - Computer Architecture. (Fall'17, Fall'16)
- - Introduction to VLSI. (Spring'17)
Microsoft Research, Redmond WA
- Research Intern Aug. 2018 to Nov. 2018
Samsung Semiconductor Inc., San Jose CA
- Research Intern May 2018 to Aug. 2018
Ph.D., New York University
M.Eng., B.Eng., Hunan University
- Deep learning, computer architecture, embedded systems, and EDA.
I am fortunate to have worked with the following people.
- Ph.D. Students
- Yaotian Liu, Fall'23 (B.S. Shanghai Jiao Tong Univ.)
- Arya Fayyazi, Fall'23 (B.S. University of Tehran)
- M.S. Students
- Fengyang Jiang, Spring'23 (B.S. Peking Univ., M.S. NYU)
- Arjun Hati, Spring'23
- Anirudh Lakshmanan, Spring'23
- Deepak Patil, Spring'23
- Join us: We’re always looking for curious and enthusiastic researchers (graduate students, postdocs, or undergraduate students) to join the team. We believe that creative research is the outcome of a collaborative, fun, and diverse work environment. If you’re interested in joining, e-mail us with your resume, a transcript, and why you’d like to join our group.
- A nearly complete list of my publications can be found via my Google Scholar page
07/2023: Our paper "Analyzing and Mitigating the Impact of Permanent Faults on DNN Accelerators" has been accepted at the IEEE Top Picks in Test and Reliability, to be held as a fringe workshop of the 2023 IEEE International Test Conference (ITC)!
07/2023: Our paper "VecPAC: A Vectorizable and Precision-Aware CGRA" has been accepted at the ICCAD'23!
06/2023: Serving as a TPC member for ICCD'23.
03/2023: We are organizing ACM SIGDA/IEEE CEDA Early Career Workshop at DAC 2023!
03/2023: Serving on the program committee of "Machine Learning and Systems Rising Stars 2023".
03/2023: Serving as the Recruitment Chair and Ph.D. forum co-chair for ESWEEK'23.
02/2023: Serving as a TPC member for ISLVSI'23.
01/2023: Joined ASU as an assistant professor of ECEE!
01/2023: Our paper "Path Planning Under Uncertainty to Localize mmWave Sources" has been accepted at the ICRA 2023!
12/2022: We'll be organizing ACM CADathlon (“Olympic games of EDA”) again at ICCAD'23;
10/2022: Serving as a TPC member for DAC'23.
10/2022: Our paper "A 12nm 18.1TFLOPs/W Sparse Transformer Processor with Entropy-Based Early Exit, Mixed-Precision Predication, and Fine-Grained Power Management" has been accepted at the ISSCC 2023!
09/2022: Serving as a session chair for "Performance, Power and Temperature Aspects in Deep Learning" at ICCAD'22. Join us at San Diego!
09/2022: Our paper "End-to-end Synthesis of Dynamically Scheduled Machine Learning Accelerators” has been accepted at IEEE Transactions on Computers.
08/2022: Our invited paper "A Scalable Methodology for Agile Chip Development with Open-Source Hardware Components" will appear at ICCAD'22.
07/2022: Presented an invited talk at DACPS 2022.
06/2022: Our paper "Bridging Python to Silicon: The SODA Toolchain" has been accepted at IEEE Micro 2022!
06/2022: Our paper "A 12nm Agile-Designed SoC for Swarm-Based Perception with Heterogeneous IP Blocks, a Reconfigurable Memory Hierarchy, and an 800MHz Multi-Plane NoC" has been accepted at ESSDERC/ESSCIRC 2022!
05/2022: Serving as an Artifact Evaluation co-chair for HPCA'23. Please submit your best work!
05/2022: We're organizing ACM SIGDA 1st Job Fair at ICCAD'22; Please submit your CV!
04/2022: Serving as a session chair for "Accelerating the Inference: Transformers, Graphs and Others" at DAC'22. Find me in San Francisco!
04/2022: We're organizing ACM CADathlon (“Olympic games of EDA”) at ICCAD'22; Please join the competition!
04/2022: Our paper "ASAP: Automatic Synthesis of Area-efficient and Precision-aware CGRA" (Collaborated with PNNL) has been accepted at ICS'22!
04/2022: Serving as a TPC member for ICCD'22.
03/2022: Two papers got accepted at DATE'22; one of them is normainated for Best Paper Award!
02/2022: We're organizing the NOPE workshop at ASPLOS 2022. Please consider submitting your NOPE papers!
02/2022: Our paper on "Millimeter Wave Wireless-assisted Robobic Navigation" has been accepted at IEEE Open Journal of the Communications Society!
12/2021: Serving as an External Reviewer for MLSys'22.
10/2021: We successfully taped out our second 12nm Domain-specific SoC for autonomous driving (in collaboration with IBM, Columbia, and UIUC)!
10/2021: Serving as a TPC member for the AI/ML Design: Circuits and Architecture track of DAC'22.
09/2021: Our RecPipe work passed the MICRO 2021 Artifact Evaluation process and received all 3 badges (Artifact Available, Functional, Reproduced)!! Please try them here!
07/2021: Presented our work "Towards automatic and agile ML/AI accelerator design with end-to-end synthesis" (Collaborated with PNNL) at ASAP'21! [Paper] [Slides]
07/2021: Our paper "RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance" has been accepted at MICRO'21!
07/2021: Serving as a TPC member on Test, security, and verficiation track of ICCD'21.
06/2021: Serving as a TPC member for IISWC'21.
11/2020: Presented two posters at NSF Workshop on Machine Learning Hardware. [Poster1][Poster 2]
10/2020: Serving as a TPC member for the AI/ML system design track of DAC'21.
10/2020: Serving as a TPC member for the architecture track of IPDPS'21.
08/2020: Two U.S. Patents on data compression for AI Hardware are published with Microsoft Research and HoloLens Team.
08/2020: Selected as a member of the Artifact Evaluation Committee for USENIX OSDI'20.
08/2020: Became an alumnus of NYU EuSuRe research group and joined the Harvard Architecture, Circuits, and Compilers Group as a Postdoctoral Fellow!
07/2020: Presented thesis work at ACM SIGDA 2020 57th Design Automation Conference (DAC) Ph.D. Forum.
07/2020: Successfully defended my Ph.D. thesis: Towards Energy Efficient and Reliable Deep Learning Inference. Thesis committee members: Prof. Siddharth Garg, Prof. Ramesh Karri, Prof. Tushar Krishna, Prof. Sundeep Rangan, and Prof. Brandon Reagen. [Slides][Video]
07/2020: Presented our "Model-Switching: Dealing with Fluctuating Workloads in MLaaS Systems" work (collaborated with Microsoft Research and Microsoft Advertising) at USENIX HotCloud'20! [Paper][Slides][Video]
06/2020: Contributed proposal on “Resource Constrained Mobile Data Analytics Assisted by the Wireless Edge” got funded by NSF and Intel!
03/2020: Participated in the semifinals for TTTC’s E. J. McCluskey Best Doctoral Thesis 2020 at IEEE VLSI Test Symposium.
03/2020: Attended and got nominated for Best Presentation Award at ACM SIGDA 2020 DATE Ph.D. Forum.
Courses
2023 Fall
| Course Number | Course Title |
|---|---|
| EEE 492 | Honors Directed Study |
| EEE 493 | Honors Thesis |
| EEE 499 | Individualized Instruction |
| EEE 590 | Reading and Conference |
| EEE 595 | Continuing Registration |
| EEE 599 | Thesis |
| EEE 690 | Reading and Conference |
| EEE 790 | Reading and Conference |
| EEE 792 | Research |
| EEE 795 | Continuing Registration |
| EEE 799 | Dissertation |
| EEE 592 | Research |
| CEN 792 | Research |
| CEN 599 | Thesis |
| EEE 792 | Research |
| EEE 592 | Research |
| EEE 590 | Reading and Conference |
| EEE 492 | Honors Directed Study |
| EEE 493 | Honors Thesis |
| EEE 499 | Individualized Instruction |
| EEE 499 | Individualized Instruction |
| EEE 598 | Special Topics |
2023 Summer
| Course Number | Course Title |
|---|---|
| EEE 690 | Reading and Conference |
| EEE 590 | Reading and Conference |
| EEE 790 | Reading and Conference |
| EEE 792 | Research |
| EEE 595 | Continuing Registration |
| EEE 599 | Thesis |
| EEE 592 | Research |
| EEE 592 | Research |
| EEE 595 | Continuing Registration |
| EEE 599 | Thesis |
| EEE 792 | Research |
| EEE 590 | Reading and Conference |
| EEE 592 | Research |
| EEE 599 | Thesis |
| EEE 792 | Research |
2023 Spring
| Course Number | Course Title |
|---|---|
| EEE 790 | Reading and Conference |
| EEE 592 | Research |
| EEE 493 | Honors Thesis |
| EEE 499 | Individualized Instruction |
| EEE 690 | Reading and Conference |
| EEE 595 | Continuing Registration |
| EEE 492 | Honors Directed Study |
| EEE 599 | Thesis |
| EEE 792 | Research |
| EEE 799 | Dissertation |
| EEE 595 | Continuing Registration |
| EEE 792 | Research |
| EEE 590 | Reading and Conference |
| EEE 795 | Continuing Registration |
| CEN 599 | Thesis |
| EEE 525 | VLSI Design |
| EEE 590 | Reading and Conference |
| EEE 525 | VLSI Design |
| EEE 492 | Honors Directed Study |
| EEE 493 | Honors Thesis |
| EEE 499 | Individualized Instruction |