Paul Franzon

Biography

My teaching and research focuses on building microsystems (systems constructed of silicon chips, both analog and digital, and silicon micomachined components) for applications in computing, communications, sensors, robotics, and signal processing.

Education

• Ph.D. 1988
  Electrical Engineering
  University of Adelaide, Australia
• Bachelor's 1984
  Electrical Engineering
  University of Adelaide, Australia
• Bachelor's 1982
  Physics and Mathematics
  University of Adelaide, Australia

Recent Publications

• A Deep Transfer Learning Design Rule Checker With Synthetic Training (2023)
• Design Obfuscation Through 3-D Split Fabrication With Smart Partitioning (2022)
• FAXID: FPGA-Accelerated XGBoost Inference for Data Centers using HLS (2022)
• Hardware Implementation of Hierarchical Temporal Memory Algorithm (2022)
• High Dimensional Optimization for Electronic Design (2022)
• Modeling of Adaptive Receiver Performance Using Generative Adversarial Networks (2022)
• RxGAN: Modeling High-Speed Receiver through Generative Adversarial Networks (2022)
• 2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2-D Deep Learning (2021)
• A 125 mu m x 245 mu m Mainly Digital UHF EPC Gen2 Compatible RFID Tag in 55 nm CMOS Process (2021)
• A Review of 3D-Dynamic Random-Access Memory based Near-Memory Computation (2021)

Involvement

• IEEE EPS Society
  Associate Editor