• Artificial Intelligence (AI) technology
  • Multi-Level (Device/Circuit/Architecture/Application) Low-Power VLSI Design
  • Intelligent Power-Efficient Mobile Video Circuits and Systems
  • Energy-Efficient and Private Memory for Deep Learning
  • Neuromorphic Computing
  • Embedded Vision

Dr. Gong's research interests lie in VLSI, embedded vision, and intelligent circuits and systems. My group is interested in exploring device, circuit, architecture, and application level solutions for developing power efficient and reliable VLSI circuits and systems, with an emphasis on energy-efficient and private data storage system for videos and deep learning applications.

We explore multi-level (device, circuit, architecture) hardware design solutions for developing power efficient and reliable VLSI Circuits and Systems.

We focus on intelligent efficient and private storage system for deep learning, multimedia, and other big-data applications.


  • Viewer-Aware Energy-Quality Adaptive Mobile Video Storage 
  • Data-Informed Efficient Memory Hardware for Edge Computing
  • ​Memory Optimization for Energy-Efficient Differentially Private Deep Learning
  • Mathematical Models Based Optimal Memory Design
  • ​Embedded Vision: Bringing Machine Intelligence to Welding Visual Inspection, Pavement Monitoring, Agriculture, Cancer Detection, and Other Different Applications


  • Artificial Intelligence (AI) technology
  • Intelligent data-enabled computing circuits and systems
  • Viewer-aware mobile systems
  • Multi-level (device/circuit/architecture/application) efficient and privacy-preserving VLSI circuits and systems
  • Energy-efficient computing
  • Memory systems for video, vision, and deep learning
  • Neuromorphic computing
  • Embedded vision
BE, Hebei University, Electrical Engineering
MS, Hebei University, Electrical Engineering
PhD, University at Buffalo, Computer Science and Engineering