About me

I am an Assistant Professor in the Electrical and Computer Engineering department at San Diego State University. After studying Computer Enginnering at Politecnico di Torino in Italy, I obtained my PhD degree in Electrical and Electronic Engineering from École Polytechnique Fédérale de Lausanne, Switzerland, in 2023. I then joined the SEELab at UC San Diego as a postdoctoral scholar and joined San Diego State University as a faculty memeber in 2025. My research interests include HW-SW co-design optimizations for edge AI, unsupervised, ensemble, and distributed learning, energy-efficient systems, and processing in-memory acceleration on emerging memory technologies.

News

• 2025 - November. Our new paper titled "Tiered Residual Quantization for LLM Vector Search in Far-Memory-Aware ANNS Systems" has been accepted at DATE 2026 🚀. Congratulations to amazing Tianqi Zhang for his great work!


• 2025 - August. I am currently looking for a highly motivated PhD student starting Fall 2026!!!
  For more info click here


• 2025 - August. I am excited to join San Diego State University as Assistant Professor in the Dept. of ECE

Research Interests

I am interested in energy-efficient design and HW-SW co-optimization for both ultra-low-power IoT systems and large-scale applications. More specific research directions include:

Ensemble learning for accurate and robust AI

HW-SW co-design for Edge AI optimizations

Distributed unsupervised learning under constrained resources

Lifelong learning in connected IoT systems

Energy-efficient optimizations of large-scale vector databases

Teaching Experience

• COMPE 361. Advanced Programming, San Diego State University
• COMPE 271. Computer Organization, San Diego State University
• CSE 147. Introduction to Embedded Systems, UC San Diego
• CSE 237a. Introduction to Embedded Computing, UC San Diego
• EE-310. Microprogrammed Embedded Systems, EPFL

Selected Publications

• Le Zhang, Onat Gungor, Flavio Ponzina, and Tajana Rosing., “E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning”, ASP-DAC, 2025


• Kumar Ashwani, Yucheng Zhou, Sai Praneeth Potladurthy, Jeoghoon Kim, Weihong Xu, Flavio Ponzina, Seounghyun Kim, Ertugrul Cubukcu, Tajana Rosing, Gert Cauwenbergh, and Duygu Kuzum. “Filament-free Bulk RRAM with High Endurance and Long Retention for Neuromorphic Few-Shot Learning On-Chip”, IEDM, 2024


• Flavio Ponzina, Rishikanth Chandrasekaran, Anya Wang, Seiji Minowada, Siddharth Sharma, and Tajana Rosing. “Multi-Model Inference Composition of Hyperdimensional Computing Ensembles”, ICCD, 2024


• Keming Fan, Ashkan Moradifirouzabadi, Xiangjin Wu, Zheyu Li, Flavio Ponzina, Anton Persson, Vikram Adve, Eric Pop, Tajana Rosing, and Mingu Kang. “SpecPCM: A Low-power PCM-based In-Memory Computing Accelerator for Full-stack Mass Spectrometry Analysis” IEEE JXCDC, 2024.


• Flavio Ponzina, Mialyssa Gomez, Congge Xu, and Tajana Rosing. “GlucoseHD Predicting Glucose Levels using Hyperdimensional Computing”, IEEE Design and Test, 2024.


• Marco Rios, Flavio Ponzina, Alexandre Levisse, Giovanni Ansaloni, and David Atienza. "Bit-line computing for CNN accelerators co-design in edge AI inference" IEEE Transactions on Emerging Topics in Computing 11, no. 2, 2023.


• Marco Rios, Flavio Ponzina, Giovanni Ansaloni, Alexandre Levisse, and David Atienza. "Running efficiently cnns on the edge thanks to hybrid sram-rram in-memory computing", DATE, 2021


• Flavio Ponzina, Miguel Peon-Quiros, Andreas Burg, and David Atienza. "E2cnns: Ensembles of convolutional neural networks to improve robustness against memory errors in edge-computing devices" IEEE Transactions on Computers 70, no. 8, 2021.