- 1018 North Engineering Research Center
- Phone (205) 348-2823
- Faculty Website
Dr. Jinhui Wang
Professor and Larry Drummond Endowed Chair
Contact
- 1018 North Engineering Research Center
- phone (205) 348-2823
- Faculty Website
Education
- Postdoctoral Fellow, University of Rochester
- Ph.D., Electrical Engineering, Beijing University of Technology
Dr. Jinhui Wang is a Professor and Larry Drummond Endowed Chair in the Department of Electrical and Computer Engineering at The University of Alabama. He is a Director of the Intelligent Multi-Level Power-Aware Circuits and sysTems (IMPACT) Lab. Throughout his academic career, Dr. Wang has secured a total of $17 million research grants including $16 million from federal agencies, NSF, DOE, and NOAA, as a Principal Investigator or Co-Principal Investigator. He has published over 200 refereed journal/conference papers and book chapters as well as 31 patents in emerging semiconductor technologies. His previous work has received the Best Paper Awards/Nominations at DATE 2021, ISVLSI 2019, ISLPED 2016, ISQED 2016, and EIT 2016. His research interests include: (1) VLSI System, Digital and Mixed-Signal Integrated Circuit (IC) Design, 3D and 2.5D IC Design, and Emerging Memory; (2) AI Hardware Design, Post/Beyond CMOS Device, such as Memristors, Based Neuromorphic Computing and Sensing System; (3) Water, Carbon, and Environmental Sustainability Computing and Sensing System, and (4) Post/Beyond CMOS Devices Enabled Cybersecurity and Internet of Things (IoT) Systems.
Affiliated Areas
Electrical and Computer Engineering
Selected Publications
- H. Uppaluru, S. Kunwar, A. Chen, and J. Wang, “Parallel Interface-type (IT) Memristor Architecture with Non-Linear Mapping and Carbon Footprint Estimation for Neuromorphic Computing Systems,” IEEE Transactions on Computers, vol. 75, no. 8, pp. 2646-2659, August 2026.
- H. Uppaluru, M. S. M. Jiban, S. Z. Riam, F. Zhao, and J. Wang, “Performance Analysis and Optimization of Fructose Memristor-Based Neuromorphic Systems,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 16, no. 2, pp. 157-168, June 2026.
- U. H. Irin, M. M. Azmir, M. R. Sarkar, C. Yang, G. Yuan, Y. Yi, and J. Wang, ” A CMOS Compatible Energy Efficient 6T NV-SRAM Based Accelerator Employing Binary Neural Networks in 22 nm FDSOI Technology,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 16, no. 2, pp. 262-275, June 2026.
- J. Fu, Z. Liao, J. Liu, S. C. Smith, and J. Wang, “Memristor Based Variation Enabled Differentially Private Learning Systems for Edge Computing in IoT,” IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9672-9682, June 2021.
- X. Peng, W. Sansen, L. Hou, J. Wang, and W. Wu, “Impedance Adapting Compensation for Low-Power Multistage Amplifiers,” IEEE Journal of Solid-State Circuits, vol. 46, no. 2, pp. 445-451, February 2011.