Giovanni Pollo

Argomento di ricerca

Design techniques for low-area digital circuits in industrial and medical applications based on machine learning

Tutori

• Enrico Macii
• Daniele Jahier Pagliari
• Sara Vinco

Presentazione della ricerca

Poster

Corso di laurea di 1° livello

• Tecniche di programmazione. A.A. 2024/25, INGEGNERIA INFORMATICA. Collaboratore del corso
• Tecniche di programmazione. A.A. 2023/24, INGEGNERIA INFORMATICA. Collaboratore del corso

• EDA - Electronic Design Automation (DAUIN)

• Albu, Andrei Mihai; Pollo, Giovanni; Burrello, Alessio; Jahier Pagliari, Daniele; ... (In stampa)
  Integrating SystemC TLM into FMI 3.0 Co-Simulations with an Open-Source Approach. In: Design and Verification Conference and Exhibition Europe, Munich (DEU), October 14-15, 2025
  Contributo in Atti di Convegno (Proceeding)
• Pollo, Giovanni; Burrello, Alessio; Macii, Enrico; Poncino, Massimo; Vinco, Sara; ... (2025)
  Coupling Neural Networks and Physics Equations For Li-Ion Battery State-of-Charge Prediction. In: Design, Automation and Test in Europe Conference and Exhibition, Lyon (FRA), 31 March 2025 - 02 April 2025, pp. 1-7. ISBN: 978-3-9826741-0-0
  Contributo in Atti di Convegno (Proceeding)
• Pollo, Giovanni; Albu, Andrei Mihai; Burrello, Alessio; Pagliari, Daniele Jahier; ... (2025)
  Automatic integration of SystemC in the FMI standard for Software-defined Vehicle design. In: Forum on specification & Design Languages (FDL) 2025, St. Goar (DEU), 10-12 September 2025, pp. 1-9. ISBN: 979-8-3315-9379-7
  Contributo in Atti di Convegno (Proceeding)
• Pollo, Giovanni; Hamdi, Mohamed Amine; Risso, Matteo; Ruotolo, Lorenzo; Furbatto, ... (2025)
  MEbots: Integrating a RISC-V Virtual Platform with a Robotic Simulator for Energy-aware Design. In: ISLPED 2025 IEEE/ACM International Symposium on Low Power Electronics and Design, Reykjavik (ISL), August 6 - 8, 2025. ISBN: 979-8-3315-2710-5
  Contributo in Atti di Convegno (Proceeding)
• Burrello, Alessio; Carlucci, Francesco; Pollo, Giovanni; Wang, Xiaying; Poncino, ... (2024)
  Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables. In: Biomedical Circuits and Systems Conference (BIOCAS) 2024, Xi'an (CHN), 24-26 October 2024, pp. 1-5. ISBN: 979-8-3503-5495-9
  Contributo in Atti di Convegno (Proceeding)