Faculty Profile

Hui Zhao

Title
Associate Professor
Department
Computer Science and Engineering
College
College of Engineering
Associate Professor
Mechanical Engineering
College of Engineering

    

Education

PhD, Pennsylvania State University, 2014.
Major: Computer Science and Engineering

Current Scheduled Teaching*

CSCE 5934.823, Directed Study, Spring 2024
CSCE 6950.923, Doctoral Dissertation, Spring 2024
CSCE 6940.923, Individual Research, Spring 2024
CSCE 5950.823, Master's Thesis, Spring 2024
CSCE 5615.001, Networks on Chip, Spring 2024

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching*

CSCE 5610.001, Computer System Architecture, Fall 2023 SPOT
CSCE 4890.723, Directed Study, Fall 2023
CSCE 5934.823, Directed Study, Fall 2023
CSCE 6950.823, Doctoral Dissertation, Fall 2023
CSCE 6940.823, Individual Research, Fall 2023
CSCE 5934.823, Directed Study, Spring 2023
CSCE 6940.723, Individual Research, Spring 2023
CSCE 5615.001, Networks on Chip, Spring 2023 Syllabus SPOT
CSCE 5610.001, Computer System Architecture, Fall 2022 Syllabus SPOT
CSCE 5934.823, Directed Study, Fall 2022
CSCE 6940.823, Individual Research, Fall 2022
CSCE 6900.823, Special Problems, Fall 2022
CSCE 5934.723, Directed Study, Spring 2022
CSCE 6940.723, Individual Research, Spring 2022
CSCE 5615.001, Networks on Chip, Spring 2022 SPOT
CSCE 6940.823, Individual Research, Fall 2021
CSCE 3600.003, Principles of Systems Programming, Fall 2021 Syllabus SPOT
CSCE 3600.205, Principles of Systems Programming, Fall 2021 SPOT
CSCE 3600.206, Principles of Systems Programming, Fall 2021 SPOT
CSCE 3600.207, Principles of Systems Programming, Fall 2021 SPOT
CSCE 3600.208, Principles of Systems Programming, Fall 2021 SPOT
CSCE 4610.001, Computer Systems Architecture, Spring 2021 Syllabus SPOT
CSCE 6950.723, Doctoral Dissertation, Spring 2021
CSCE 6940.723, Individual Research, Spring 2021
CSCE 3600.003, Principles of Systems Programming, Spring 2021 Syllabus SPOT
CSCE 3600.281, Principles of Systems Programming, Spring 2021 SPOT
CSCE 3600.282, Principles of Systems Programming, Spring 2021 SPOT
CSCE 3600.283, Principles of Systems Programming, Spring 2021 SPOT
CSCE 4999.723, Senior Thesis, Spring 2021
CSCE 6950.823, Doctoral Dissertation, Fall 2020
CSCE 6940.823, Individual Research, Fall 2020
CSCE 3600.001, Principles of Systems Programming, Fall 2020 Syllabus SPOT
CSCE 3600.004, Principles of Systems Programming, Fall 2020 Syllabus SPOT
CSCE 6950.723, Doctoral Dissertation, Spring 2020
CSCE 6940.723, Individual Research, Spring 2020
CSCE 5615.001, Networks on Chip, Spring 2020
CSCE 2610.001, Assembly Language and Computer Organization, Fall 2019 Syllabus SPOT
CSCE 2610.201, Assembly Language and Computer Organization, Fall 2019 SPOT
CSCE 2610.202, Assembly Language and Computer Organization, Fall 2019 SPOT
CSCE 2610.203, Assembly Language and Computer Organization, Fall 2019 SPOT
CSCE 2610.204, Assembly Language and Computer Organization, Fall 2019 SPOT
CSCE 2610.205, Assembly Language and Computer Organization, Fall 2019 SPOT
CSCE 6940.823, Individual Research, Fall 2019
CSCE 5934.823, Directed Study, Summer 10W 2019
CSCE 6940.823, Individual Research, Summer 10W 2019
CSCE 5900.823, Special Problems, Summer 10W 2019
CSCE 5910.823, Special Problems, Summer 10W 2019
CSCE 6940.823, Individual Research, Spring 2019
CSCE 5615.001, Networks on Chip, Spring 2019 SPOT
CSCE 2610.001, Assembly Language and Computer Organization, Fall 2018 Syllabus SPOT
CSCE 6940.823, Individual Research, Fall 2018
CSCE 5934.823, Directed Study, Summer 5W2 2018
CSCE 6940.823, Individual Research, Summer 10W 2018
CSCE 6940.823, Individual Research, Spring 2018
CSCE 5933.007, Topics in Computer Science and Engineering, Spring 2018 SPOT
CSCE 5610.001, Computer System Architecture, Fall 2017 SPOT
CSCE 5610.600, Computer System Architecture, Fall 2017 SPOT
CSCE 6940.823, Individual Research, Fall 2017
CSCE 5933.007, Topics in Computer Science and Engineering, Spring 2017 SPOT
CSCE 5610.001, Computer System Architecture, Fall 2016 SPOT

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Publications

Published Intellectual Contributions

Conference Proceeding
Liu, Z., Zhang, S., Garrigus, J., Zhao, H. (2023). Genomics-GPU: A Benchmark Suite for GPU-Accelerated Genome Analysis. ISPASS 2023: 178-188. Raleigh, North Carolina April 23-25, 2023.: 2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS2023).
Ho, K., Zhao, H., Jog, A., Mohanty, S. (2022). Improving GPU Throughput Through Parallel Execution Using Tensor Cores and CUDA Cores. 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI).
Liu, Z., Exley, T., Geek, A., Yang, R., Zhao, H., Albert, M. (2022). Predicting GPU Performance and System Parameter Configuration Using Machine Learning. 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI).
Kandemir, M. T., Tang, X., Zhao, H., Ryoo, J., Karakoy, M. (2021). Distance-in-time versus distance-in-space. Association for Computing Machinery,New York NY,United States.
Jiang, B., Cheng, X., Tang, S., Ma, X., Gu, Z., Zhao, H., Fu, S. (2021). APCNN: Explore Multi-Layer Cooperation for CNN Optimization and Acceleration on FPGA. The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. https://dl.acm.org/doi/abs/10.1145/3431920.3439461
Cui, Y., Prabhakar, S., Zhao, H., Mohanty, S., Fang, J. (2020). A Low-Cost Conflict-Free NoC Architecture for Heterogeneous Multicore Systems (ISVLSI).
Cheng, X., Zhao, H., Kandemir, M., Mohanty, S., Jiang, B. (2020). Alleviating Bottlenecks for DNN Execution on GPUs via Opportunistic Computing (ISQED).
Cheng, X., Zhao, H., Kandemir, M., Jiang, B., Mehta, G. (2020). AMOEBA: a coarse-grained reconfigurable architecture for dynamic GPU scaling. The 34th ACM International Conference on Supercomputing. https://dl.acm.org/doi/abs/10.1145/3392717.3392738
Kandemir, M., Ryoo, ., Zhao, H., Jung, M., Karakoy, M. (2020). Collective Affinity Aware Computation Mapping (PACT).
Fang, J., Zhang, J., Lu, S., Zhao, H. (2020). Exploration on Task Scheduling Strategy for CPU-GPU Heterogeneous Computing System (ISVLSI).
Cheng, X., Zhao, H., Kandemir, M., Jiang, B., Mehta, G. (2020). AMOEBA: A Coarse Grained Reconfigurable Architecture for Dynamic GPU Scaling. Proceedings of the 34th ACM International Conference on Supercomputing.
Cheng, X., Zhao, H., Kandemir, M., Mohanty, S., Jiang, B. (2020). Alleviating Bottlenecks for DNN Execution on GPUs via Opportunistic Computing. 2020 21st International Symposium on Quality Electronic Design (ISQED). https://ieeexplore.ieee.org/abstract/document/9136967
Fang, J., Zhou, K., Zhao, H. (2019). Dynamic block size adjustment and workload balancing strategy based on CPU-GPU heterogeneous platform. ISPA 2019.
Tang, X., Kandemir, M., Zhao, H., Jung, M., Karakoy, M. (2019). Computing with Near Data (abstract). SIGMETRICS (Abstracts) 2019: 27-28.
Zhang, L., Cheng, X., Zhao, H., Mohanty, S., Fang, J. (2019). Exploration of System Configuration in Effective Training of CNNs on GPGPUs. 2019 IEEE International Conference on Consumer Electronics (ICCE). 1--4.
Cheng, X., Zhao, H., Mohanty, S., Fang, J. (2019). Improving GPU NoC Power Efficiency through Dynamic Bandwidth Allocation. 2019 IEEE International Conference on Consumer Electronics (ICCE). 1--4.
Cheng, x., Zhao, Y., Robaei, M., Jiang, B., Zhao, H., Fang, J. (2019). A Low-Cost and Energy-Efficient NoC Architecture for GPGPUs.. ANCS.
Cheng, Y., Fang, J., Zhao, H. (2019). A Congestion-adaptive Fault-tolerant Routing Algorithm on HNoC. IEEE Cyber.
Zhao, H., Cheng, X., Mohanty, S., Fang, J. (2018). Designing Scalable Hybrid Wireless NoC for GPGPUs. 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). 703--708.
Fang, J., Chang, Z., Cheng, Y., Zhao, H. (2018). Exploration on Routing Configuration of HNoC with Reasonable Energy Consumption. 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). 744-749.
Cheng, X., Zhao, Y., Zhao, H., Xie, Y. (2018). Packet Pump: Overcoming Network Bottleneck in On-Chip Interconnects for GPGPUs. Design Automation Conference. 84:1-84:6.
Sharifi, A., Ding, W., Guttman, D., Zhao, H., Tang, X., Kandemir, M., Das, C. (2017). DEMM: A Dynamic Energy-Saving Mechanism for Multicore Memories. MASCOTS 2017: 210-220. MASCOTS. 210-220.
Journal Article
Fang, J., Zhang, J., Lu, S., Zhao, H., Zhang, D., Cui, Y. (2022). Task Scheduling Strategy for Heterogeneous Multicore Systems.
Fang, J., Lu, J., Wang, M., Zhao, H. (2019). A Performance Conserving Approach for Reducing Memory Power Consumption in Multi-Core Systems. Journal of Circuits, Systems, and Computers 28(7): 1950113:1-1950113:16 (2019). Journal of Circuits, Systems, and Computers. DOI: 10.1142/S0218126619501135.
Cheng, X., Zhao, H., Kandemir, M., Mohanty, S., Jiang, B. (2019). Alleviating Bottlenecks for DNN Execution on GPUs via Opportunistic Computing. Other.
Robaei, M., Zhao, H., (2019). Broadcast-Based Hybrid Wired-Wireless Network-on-Chip for GPGPUs. IEEE Consumer Electronics Magazine 8(6): 62-67 (2019). Consumer Electronics Magazine.
Fang, J., Hao, X., Fan, Q., Li, K., Zhao, H. (2019). Efficient Data Transfer in a Heterogeneous Multicore-Based CE Systems using Cache Performance Optimization. IEEE Consumer Electronics Magazine 8(5): 46-50 (2019). Consumer Electronics Magazine.
Tang, X., Kandemir, M., Zhao, H., Jung, M., Karakoy, M. (2018). Computing with Near Data. POMACS 2(3): 42:1-42:30 (2018).

Awarded Grants

Contracts, Grants and Sponsored Research

Grant - Research
Zhao, H., "CAREER: Reinventing Network-on-Chips for GPU-Accelerated Systems," Sponsored by NSF, Federal, $519038 Funded. (20212025).
Zhao, H., Albert, M., "REU Site: Interdisciplinary Research Experience on Accelerated Deep Learning through A Hardware-Software Collaborative Approach," Sponsored by NSF, Federal, $398684 Funded. (20212024).
Zhao, H. (Principal), Zhao, D. (Principal), Arigong, B. (Principal), "Collaborative Research: SHF: Small: Tangram: Scaling into the Exascale Era with Reconfigurable Aggregated "Virtual Chips"," Sponsored by NSF, Federal, $172573 Funded. (July 2020June 2023).
Kavi, K. M. (Principal), Fu, S. (Co-Principal), Zhao, H. (Co-Principal), "MRI Collaborative: Development of ESPRIT - Emerging systems’ performance and energy evaluation instruments and testbench," Sponsored by Cost-Share from UNT for a NSF Grant, University of North Texas, $128572 Funded. (September 1, 2018August 31, 2021).
Kavi, K. M. (Principal), Fu, S. (Co-Principal), Zhao, H. (Co-Principal), "MRl Collaborative: Development of ESPRIT - Emerging systems' performance and energy evaluation instruments and testbench," Sponsored by National Science Foundation, Federal, $300001 Funded. (September 2018August 2021).
Zhao, H. (Principal), "Enhancing Power and Performance Efficiency in Brain Inspired Intelligent Systems through Optimized Accelerator Design," University of North Texas, $5231 Funded. (June 2019June 2021).
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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