Hui Zhao

Previous Scheduled Teaching

Published Intellectual Contributions

Conference Proceeding

• Liu, Z., Zhang, S., Zhao, H. (2024). Survey of Hardware Acceleration of Genomic Analysis. IEEE International Symposium on Embedded Multicore Socs (MCSoC).
• Biglari, S., Hosseini, F., Upadhyay, A., Zhao, H. (2024). Survey of Network-on-Chip (NoC) for Heterogeneous Multicore Systems. IEEE International Symposium on Embedded Multicore Socs (MCSoC). https://ieeexplore.ieee.org/xpl/conhome/10819468/proceeding
• Biglari, S., Huang, R., Zhao, H., Mohanty, S., Partin-Vaisband, I., Katkoori, S., Peng, L., Vaisband, B., Nikoubin, T. (2024). Designing Reconfigurable Interconnection Network of Heterogeneous Chiplets Using Kalman Filter. Proceedings of the Great Lakes Symposium on VLSI 2024, GLSVLSI 2024, Clearwater, FL, USA, June 12-14, 2024. 663--668. ACM. https://doi.org/10.1145/3649476.3660389
• Biglari, S., Huang, R., Zhao, H., Mohanty, S. (2024). Designing Reconfigurable Interconnection Network of Heterogeneous Chiplets Using Kalman Filter. Proceedings of the Great Lakes Symposium on VLSI 2024. 663-668. ACM. https://doi.org/10.1145/3649476.3660389
• 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, J., 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).

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"," sponsored by NSF, Federal, $172573 Funded. (2020 - 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. (2018 - 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. (2018 - 2021).
• Zhao, H. (Principal), "Enhancing Power and Performance Efficiency in Brain Inspired Intelligent Systems through Optimized Accelerator Design," University of North Texas, $5231 Funded. (2019 - 2021).
• Zhao, H. (Principal), "CAREER: Reinventing Network-on-Chips for GPU-Accelerated Systems," sponsored by National Science Foundation, FED, Funded. (2021 - 2026).
• Zhao, H. (Principal), Albert, M.V. (Principal), "REU Site: Interdisciplinary Research Experience on Accelerated Deep Learning through A Hardware-Software Collaborative Approach," sponsored by National Science Foundation, FED, Funded. (2021 - 2024).
• Zhao, H. (Principal), "Collaborative Research: SHF: Small: Tangram: Scaling into the Exascale Era with Reconfigurable Aggregated "Virtual Chips"," sponsored by National Science Foundation, FED, Funded. (2020 - 2023).
• Zhao, H. (Co-Principal), Kavi, K.M. (Principal), Fu, S. (Co-Principal), "MRI Collaborative: Development of ESPRIT - Emerging Systems' Performance and Energy Evaluation Instruments and Testbench," sponsored by National Science Foundation, FED, Funded. (2018 - 2022).