Faculty Profile

Hui Zhao

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

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


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





          

        
                

          
Current Scheduled Teaching*


          
CSCE 5934.723, Directed Study, Spring 2022 
CSCE 6940.723, Individual Research, Spring 2022 
CSCE 5615.001, Networks on Chip, Spring 2022 


          
*
  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 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
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). 
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. 
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. 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., 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. (2021 – 2025).
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. (2021 – 2024).
Zhao, H. (Principal), Zhao, D. (Principal), Arigong, B. (Principal), "Collaborative Research: SHF: Small: Tangram: Scaling into the Exascale Era with Reconfigurable Aggregated "Virtual Chips"," Federal, $172573 Funded. (July 2020 – June 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, 2018 – August 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 2018 – August 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 2019 – June 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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