Dr. Flavio Ponzina

Flavio Ponzina

Assistant Professor
Academic Affairs
College of Engineering
Department of Electrical and Computer Engineering

SDSU

Email

Primary Email: [email protected]

Building/Location

Engineering - 410
5500 Campanile Drive
San Diego, CA 92182

Website Links

Bio

Flavio Ponzina is an Assistant Professor in the Electrical and Computer Engineering department at San Diego State University. He obtained his PhD degree in Electrical and Electronic Engineering from École Polytechnique Fédérale de Lausanne in 2023, and he then joined the SEELab at UC San Diego as a postdoctoral scholar. His 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.

Education

Ph.D., École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, 2023.

Areas of Specialization

  • Embedded AI
  • Energy-Efficient ML
  • HW-SW co-design
  • Processing In-Memory

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.