Paul Franzon
paulf@ncsu.edu
Monteith Research Center (MRC) 443
Campus Box 7911
Website
YouTube Channel
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
Research Focus
- Computer Architecture and Systems
- Electronic Circuits and Systems
- Networking
- Physical Electronics, Photonics & Magnetics
Funded Research
- PFI:TT: Embedded Radio Frequency Identification (RFID) to Secure the Semiconductor Supply Chain
- High Dimensional Inverse Methods for Electronic System Design, Center for Advanced Electronics Through Machine Learning (CAEML) Core Project
- GAN generated models for Signal Integrity, Thermal Modeling, Etc., Center for Advanced Electronics through Machine Learning (CAEML) Core Project 5A3
- Cirrus Logic Mixed Signal IC Design Fellowship
- FPGA Hardware Accelerator for Real Time Security, CAEML Core Project
- Differential Power Analysis of Deep Neural Networks with Mitigation at the Architecture Level
- Three Dimensional Study of the Benefits of Hybrid Bonding
- High-Dimensional Structural Inference for Non-Linear Deep Markov or State Space Time Series Models, Center for Advanced Electronics through Machine Learning (CAEML) Core Project 3A4
- Fast, Accurate PPA Model-Extraction, Center for Advanced Electronics through Machine Learning (CAEML) Core Project 3A2 funded with industry membership dues.
- Applying Machine Learning to Back End IC Design, Center for Advanced Electronics through Machine Learning (CAEML) Core Project 2A7 funded with industry membership dues.
- Trusted Fabrication through 3D Integration Demonstration
- 2.5D Extendible Processor
- RER-3D: A Research Center for Radiation Effects Reliability Mechanisms Unique to 3D Integration (Radiation Effects in 3D Structures)
- Intellectual Property Reuse through Machine Learning, Center for Advanced Electronics through Machine Learning (CAEML) Core Project 1A2 funded with industry membership dues.
- Behavioral Modeling for High Speed Links, Center for Advanced Electronics through Machine Learning (CAEML) Core Project 1A5 funded with industry membership dues.
- SHF:Small:AC Powered Digital Circuits
- Modular, Testable, Tightly Coupled 3D Implementation of a Heterogenous Processor
Books
Highlighted Awards
Awards & Honors
- 2007 - Australian Defence Medal
- 2007, 2005 - ECE Graduate Teacher of the Year Award (NC State)
- 2007 - ECE Most Helpful Teacher of the Year Award (NC State)
- 2006 - ECE Teacher of the Year Award (NC State)
- 2006 - IEEE Fellow
- 2005 - ALCOA Distinguished Research Award (NC State)
- 2003 - Alumni Undergraduate Distinguished Professor (NC State)
- 2003 - NSW Australia Expatriate Scientist Award
- 2001 - Academy of Outstanding Teachers (NC State)
- 1997 - IEEE Student Branch, Teacher of the Year (NC State)
- 1993 - NSF Young Investigators Award
- 1990 - US Army Commendation Medal
Recent News
New Approach From ECE Researchers Allows for Faster Ransomware Detection
Posted on May 19, 2022 | Filed Under: Research
NC State Electrical and Computer Engineering researchers have developed a new approach for implementing ransomware detection techniques, allowing them to detect a broad range of ransomware far more quickly than previous systems.
Center for Advanced Electronics through Machine Learning (CAEML) Receives Phase II Funding from NSF
Posted on May 12, 2022 | Filed Under: Research
The NSF has granted funding for the Center for Advanced Electronics through Machine Learning (CAEML) for its second five-year phase of research.
Franzon Selected for Qualcomm Faculty Award
Posted on May 21, 2021 | Filed Under: Faculty
Dr. Paul Franzon has been awarded a 2021 Qualcomm Faculty Awards for research that is inspirational to graduate students and serves to spark new approaches in key technology areas.
Recent Media Mentions
AI Expands Role in Design
January 31, 2018
Machine Learning Offers Helping Hand To Edit Chips
July 6, 2017
It might not fit electronic design automation at all, said Paul Franzon, a professor of electrical and computer engineering at North Carolina State University.
New NCSU research leads to 25% smaller RFID tags
May 9, 2016
Engineering researchers at North Carolina State University have developed a suite of techniques that allow them to create passive radio-frequency identification (RFID) tags that are 25 percent smaller and therefore less expensive. Dr Paul Franzon featured.
Recent Publications
- 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)
- A Scalable Cluster-based Hierarchical Hardware Accelerator for a Cortically Inspired Algorithm (2021)
- Design Benefits of Hybrid Bonding for 3D Integration (2021)
- Fast and Accurate PPA Modeling with Transfer Learning (2021)
- Fast and Accurate PPA Modeling with Transfer Learning (2021)
- High Speed Receiver Modeling Using Generative Adversarial Networks (2021)
- Multi-ANN embedded system based on a custom 3D-DRAM (2021)
- Application of Quantum Machine Learning to VLSI Placement (2020)