headshot of Peipei Zhou

Peipei Zhou

Assistant Professor
peipei.zhou@pitt.edu
Research Website Google Scholar Electrical and Computer Engineering

overview

I joined the University of Pittsburgh, ECE department as a Tenure-Track Assistant Professor starting September 2021. I obtained my Ph.D. in Computer Science from University of California, Los Angeles in 2019 supervised by Prof. Jason Cong, who leads UCLA VAST(VLSI Architecture, Synthesis and Technology) Group. My major interest is in Customized Architecture and Programming Abstraction for Applications including Healthcare, e.g., Precision Medicine and Artificial Intelligence. I’m honored to receive “Outstanding Recognition in Research” from UCLA Samueli School of Engineering in 2019. I have also received 2019 TCAD Donald O. Pederson Best Paper Award in recognition of best paper published in the IEEE Transactions on CAD in the two calendar years preceding the award. My paper has also received 2018 ICCAD Best Paper Nominee, 2018 ISPASS Best Paper Nominee.

I’m actively recruiting self-motivated graduate student and undergraduate researchers! Students with relevant research and project experience (compiler, GPU and FPGA programming, artificial intelligence algorithm and application development, etc.) are highly encouraged to contact me via email.

Personal website: http://peipeizhou-eecs.github.io/

about

PhD, University of California, Los Angeles, 2014 - 2019

MS, University of California, Los Angeles, 2012 - 2014

BS, Southeast University, 2008 - 2012

Wu, Y., Tang, Y., Zeng, D., Zhang, X., Zhou, P., Shi, Y., & Hu, J. (2024). Efficient Hardware and Software Design for On-device Learning. In Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing, Sudeep, P., & Shafique, M. (Eds.). (pp. 371-404).Springer Nature Switzerland. doi: 10.1007/978-3-031-39932-9_15.

Ollivier, S., Li, S., Tang, Y., Cahoon, S., Caginalp, R., Chaudhuri, C., Zhou, P., Tang, X., Hu, J., & Jones, A.K. (2023). Sustainable AI Processing at the Edge. IEEE MICRO, 43(1), 19-28.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/MM.2022.3220399.

Tang, Y., Wu, Y., Zhou, P., & Hu, J. (2022). Enabling Weakly Supervised Temporal Action Localization From On-Device Learning of the Video Stream. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 41(11), 3910-3921.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TCAD.2022.3197536.

Tang, Y., Zhang, X., Zhou, P., & Hu, J. (2022). EF-Train: Enable Efficient On-device CNN Training on FPGA through Data Reshaping for Online Adaptation or Personalization. ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS, 27(5), 1-36.Association for Computing Machinery (ACM). doi: 10.1145/3505633.

Zhang, X., Wu, Y., Zhou, P., Tang, X., & Hu, J. (2021). Algorithm-hardware Co-design of Attention Mechanism on FPGA Devices. ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 20(5), 1-24.Association for Computing Machinery (ACM). doi: 10.1145/3477002.

Zhang, C., Sun, G., Fang, Z., Zhou, P., Pan, P., & Cong, J. (2019). Caffeine: Toward Uniformed Representation and Acceleration for Deep Convolutional Neural Networks. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 38(11), 2072-2085.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TCAD.2017.2785257.

Li, Y., Zhao, K., Zhao, J., Wang, Q., Zhong, S., Lalam, N., Wright, R., Zhou, P., & Chen, K.P. FiberFlex: Real-time FPGA-based Intelligent & Distributed Fiber Sensor System for Pedestrian Recognition. ACM Transactions on Reconfigurable Technology and Systems.Association for Computing Machinery (ACM). doi: 10.1145/3690389.

Zhuang, J., Lau, J., Ye, H., Yang, Z., Ji, S., Lo, J., Denolf, K., Neuendorffer, S., Jones, A., Hu, J., Shi, Y., Chen, D., Cong, J., & Zhou, P. CHARM 2.0: Composing Heterogeneous Accelerators for Deep Learning on Versal ACAP Architecture. ACM Transactions on Reconfigurable Technology and Systems.Association for Computing Machinery (ACM). doi: 10.1145/3686163.

Yang, Z., Ji, S., Chen, X., Zhuang, J., Zhang, W., Jani, D., & Zhou, P. (2024). Challenges and Opportunities to Enable Large-Scale Computing via Heterogeneous Chiplets. In 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC).IEEE. doi: 10.1109/asp-dac58780.2024.10473961.

Zhuang, J., Yang, Z., Ji, S., Huang, H., Jones, A.K., Hu, J., Shi, Y., & Zhou, P. (2024). SSR: Spatial Sequential Hybrid Architecture for Latency Throughput Tradeoff in Transformer Acceleration. In Proceedings of the 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays.ACM. doi: 10.1145/3626202.3637569.

Yang, Z., Zhuang, J., Yin, J., Yu, C., Jones, A.K., & Zhou, P. (2023). AIM: Accelerating Arbitrary-Precision Integer Multiplication on Heterogeneous Reconfigurable Computing Platform Versal ACAP. In 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD).IEEE. doi: 10.1109/iccad57390.2023.10323754.

Zhang, C., Sun, G., Fang, Z., Zhou, P., & Cong, J. (2023). Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks. In Proceedings of the ACM Turing Award Celebration Conference - China 2023.ACM. doi: 10.1145/3603165.3607390.

Zhou, P., Zhuang, J., Cahoon, S., Tang, Y., Yang, Z., Chen, X., Shi, Y., Hu, J., & Jones, A.K. (2023). REFRESH FPGAs: Sustainable FPGA Chiplet Architectures. In Proceedings of the 14th International Green and Sustainable Computing Conference.ACM. doi: 10.1145/3634769.3634798.

Zhuang, J., Lau, J., Ye, H., Yang, Z., Du, Y., Lo, J., Denolf, K., Neuendorffer, S., Jones, A., Hu, J., Chen, D., Cong, J., & Zhou, P. (2023). CHARM: C omposing H eterogeneous A ccele R ators for M atrix Multiply on Versal ACAP Architecture. In Proceedings of the 2023 ACM/SIGDA International Symposium on Field Programmable Gate Arrays.ACM. doi: 10.1145/3543622.3573210.

Zhuang, J., Yang, Z., & Zhou, P. (2023). High Performance, Low Power Matrix Multiply Design on ACAP: from Architecture, Design Challenges and DSE Perspectives. In 2023 60th ACM/IEEE Design Automation Conference (DAC).IEEE. doi: 10.1109/dac56929.2023.10247981.

Zhang, X., Hao, C., Zhou, P., Jones, A., & Hu, J. (2022). H2H. In Proceedings of the 59th ACM/IEEE Design Automation Conference, 12, (pp. 601-606).ACM. doi: 10.1145/3489517.3530509.

Zhou, P., Sheng, J., Yu, C.H., Wei, P., Wang, J., Wu, D., & Cong, J. (2021). MOCHA. In The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, (pp. 273-279).ACM. doi: 10.1145/3431920.3439304.

Lo, M., Fang, Z., Wang, J., Zhou, P., Chang, M.C.F., & Cong, J. (2020). Algorithm-Hardware Co-design for BQSR Acceleration in Genome Analysis ToolKit. In 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 523, (pp. 157-166).IEEE. doi: 10.1109/fccm48280.2020.00029.

Chi, Y., Cong, J., Wei, P., & Zhou, P. (2018). SODA. In Proceedings of the International Conference on Computer-Aided Design.ACM. doi: 10.1145/3240765.3240850.

Cong, J., Wei, P., Yu, C.H., & Zhou, P. (2018). Latte: Locality Aware Transformation for High-Level Synthesis. In 2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 1, (pp. 125-128).IEEE. doi: 10.1109/fccm.2018.00028.

Ruan, Z., He, T., Li, B., Zhou, P., & Cong, J. (2018). ST-Accel: A High-Level Programming Platform for Streaming Applications on FPGA. In 2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM).IEEE. doi: 10.1109/fccm.2018.00011.

Zhou, P., Ruan, Z., Fang, Z., Shand, M., Roazen, D., & Cong, J. (2018). Doppio: I/O-Aware Performance Analysis, Modeling and Optimization for In-memory Computing Framework. In 2018 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).IEEE. doi: 10.1109/ispass.2018.00011.

Cong, J., Wei, P., Yu, C.H., & Zhou, P. (2017). Bandwidth Optimization Through On-Chip Memory Restructuring for HLS. In Proceedings of the 54th Annual Design Automation Conference 2017, (pp. 1-6).ACM. doi: 10.1145/3061639.3062208.

Zhang, C., Fang, Z., Zhou, P., Pan, P., & Cong, J. (2016). Caffeine. In Proceedings of the 35th International Conference on Computer-Aided Design.ACM. doi: 10.1145/2966986.2967011.

Zhou, P., Park, H., Fang, Z., Cong, J., & DeHon, A. (2016). Energy Efficiency of Full Pipelining: A Case Study for Matrix Multiplication. In 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 24, (pp. 172-175).IEEE. doi: 10.1109/fccm.2016.50.

Cong, J., Huang, H., Ma, C., Xiao, B., & Zhou, P. (2014). A Fully Pipelined and Dynamically Composable Architecture of CGRA. In 2014 IEEE 22nd Annual International Symposium on Field-Programmable Custom Computing Machines.IEEE. doi: 10.1109/fccm.2014.12.