Dr. Na Gong

Education

• Ph.D. in Computer Science & Engineering, University at Buffalo, the State University of New York (SUNY)
• M.S. in Electrical Engineering, Hebei University, China
• B.E. in Electrical Engineering, Hebei University, China

Research

• Artificial Intelligence (AI) Technology and Edge AI
• Multi-Level (Device/Circuit/Architecture/Application) VLSI Design
• Memory Design for Privacy Protection
• Intelligent Power-Efficient Mobile Video Circuits and Systems
• Neuromorphic Computing

Publications

• H. Das, A. Haidous, S. Smith, and N. Gong, 鈥淔lexible Low-Cost Power-Efficient Video Memory with ECC-Adaptation,鈥� IEEE Trans. on Very Large Scale Integration (VLSI) Systems, vol. 29, no. 10, pp. 1693 鈥� 1706, Aug. 2021.
• J. Edstrom, H. Das, Y. Xu, and N. Gong, 鈥淢emory Optimization for Energy-Efficient Differentially Private Deep Learning,鈥� IEEE Trans. on Very Large Scale Integration (VLSI) Systems, vol. 28, no. 2, pp. 307-316, Feb. 2020.
• Y. Xu, D. Hritom, Y. Gong and N. Gong, 鈥淥n Mathematical Models of Optimal Video Memory Design,鈥� IEEE Trans. on Circuits and Systems for Video Technology, vol. 30, no. 1, pp. 256-266, Jan. 2020.
• J. Fu, Z. Liao, N. Gong, and J. Wang, 鈥淢itigating Nonlinear Effect of Memristive Synaptic Device for Neuromorphic Computing,鈥� IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 9, no. 2, pp. 377-387, June 2019.
• J. Edstrom, D. Chen, Y. Gong, J. Wang, and N. Gong, 鈥淒ata-Pattern Enabled Self-Recovery Low-Power Storage System for Big Video Data,鈥� IEEE Trans. on Big Data, vol. 5, no. 1, pp. 95-105, March 2019.

For a complete list of publications, please see my Personal Website.

Courses

• EE 560 Advanced Computer Architecture
• EE 454/554 Digital Computer Architecture
• EE 443/543 HDL Logic Simulation
• EE 440/540 HDL Logic Synthesis