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Beilei Jiang

Title: Clinical Assistant Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, University of North Texas, 2022
    Major: Computer Science and Engineering
    Dissertation: Deep Learning Optimization and Acceleration

Current Scheduled Teaching

CSCE 5150.001Analysis of Computer AlgorithmsSpring 2025
CSCE 5150.005Analysis of Computer AlgorithmsSpring 2025
CSCE 5610.001Computer System ArchitectureFall 2024
CSCE 5610.002Computer System ArchitectureFall 2024
CSCE 5610.003Computer System ArchitectureFall 2024
CSCE 5610.600Computer System ArchitectureFall 2024
CSCE 4620.001Real Time Operating SystemsFall 2024 Syllabus
CSCE 5620.001Real Time Operating SystemsFall 2024
CSCE 5933.004Topics in Computer Science and EngineeringFall 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 5150.002Analysis of Computer AlgorithmsSummer 10W 2024 SPOT
CSCE 5610.001Computer System ArchitectureSummer 10W 2024 SPOT
CSCE 5610.002Computer System ArchitectureSummer 10W 2024 SPOT
CSCE 5150.001Analysis of Computer AlgorithmsSpring 2024 SPOT
CSCE 5150.005Analysis of Computer AlgorithmsSpring 2024 SPOT
CSCE 5150.004Analysis of Computer AlgorithmsFall 2023 SPOT
CSCE 5150.005Analysis of Computer AlgorithmsFall 2023 SPOT
CSCE 5610.002Computer System ArchitectureFall 2023 SPOT
CSCE 4620.001Real Time Operating SystemsFall 2023 Syllabus SPOT
CSCE 5620.001Real Time Operating SystemsFall 2023 SPOT
CSCE 5150.002Analysis of Computer AlgorithmsSummer 10W 2023 SPOT
CSCE 5610.001Computer System ArchitectureSummer 10W 2023 SPOT
CSCE 3110.001Data Structures and AlgorithmsSummer 10W 2023 Syllabus SPOT
CSCE 5150.001Analysis of Computer AlgorithmsSpring 2023 SPOT
CSCE 5150.005Analysis of Computer AlgorithmsSpring 2023 SPOT
CSCE 3110.001Data Structures and AlgorithmsSpring 2023 Syllabus SPOT
CSCE 3110.002Data Structures and AlgorithmsSpring 2023 Syllabus SPOT
CSCE 1035.001Computer Programming IFall 2022 Syllabus SPOT
CSCE 1035.306Computer Programming IFall 2022 SPOT
CSCE 5610.002Computer System ArchitectureFall 2022 Syllabus SPOT
CSCE 4620.001Real Time Operating SystemsFall 2022 Syllabus SPOT
CSCE 5620.001Real Time Operating SystemsFall 2022 Syllabus SPOT
CSCE 5620.002Real Time Operating SystemsFall 2022 Syllabus 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 Intellectual Contributions

    Conference Proceeding

  • Jiang, B., Cheng, X., Li, Y., Fu, S., Yang, Q., Liu, M., Olvera, A. (2023). Output-Directed Dynamic Quantization for DNN Acceleration. 645-654. International Conference on Parallel Processing (ICPP).
  • Jiang, B., Cheng, X., Tang, S., Ma, X., Gu, Z., Fu, S., Yang, Q., Liu, M. (2022). MLCNN: Cross-Layer Cooperative Optimization and Accelerator Architecture for Speeding Up Deep Learning Applications.. International Parallel and Distributed Processing Symposium (IPDPS. 1184-1194. https://ieeexplore.ieee.org/abstract/document/9820611
  • Gu, Z., Tang, S., Jiang, B., Huang, S., Guan, Q., Fu, S. (2021). Characterizing Job-Task Dependency in Cloud Workloads Using Graph Learning. 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). https://ieeexplore.ieee.org/abstract/document/9460684
  • 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
  • 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
  • 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
  • Cheng, X., Zhao, Y., Robaei, M., Jiang, B., Zhao, H., Fang, J. (2019). A low-cost and energy-efficient noc architecture for GPGPUs. ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS).
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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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