Dottorato in Intelligenza Artificiale , 37o ciclo (2021-2024)
Dottorato concluso nel 2025
Tesi:
On-device Learning and Inference Optimization for Lightweight Neural Networks and Transformers on Microcontrollers (Abstract)
Tutori:
Francesco ContiLuca BeniniProfilo
Interessi di ricerca
Pubblicazioni
Pubblicazioni durante il dottorato Vedi tutte le pubblicazioni su Porto@Iris
- Dequino, Alberto (2025)
On-device Learning and Inference Optimization for Lightweight Neural Networks and Transformers on Microcontrollers. relatore: CONTI, FRANCESCO; BENINI, LUCA; , 37. XXXVII Ciclo, P.: 112
Doctoral Thesis - Dequino, Alberto; Bompani, Luca; Benini, Luca; Conti, Francesco (2025)
Optimizing BFloat16 Deployment of Tiny Transformers on Ultra-Low Power Extreme Edge SoCs. In: JOURNAL OF LOW POWER ELECTRONICS AND APPLICATIONS, vol. 15. ISSN 2079-9268
Contributo su Rivista - Dequino, Alberto; Carpegna, Alessio; Nadalini, Davide; Savino, Alessandro; Benini, Luca; ... (2024)
Compressed Latent Replays for Lightweight Continual Learning on Spiking Neural Networks. In: 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Knoxville, TN (USA), 01-03 July 2024, pp. 240-245. ISBN: 979-8-3503-5411-9
Contributo in Atti di Convegno (Proceeding) - Dequino, A; Conti, Francesco; Benini, Luca (2022)
ViT-LR: Pushing the Envelope for Transformer-Based On-Device Embedded Continual Learning. In: 2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC), Pittsburgh, PA (USA), 24-25 October 2022, pp. 1-6. ISBN: 978-1-6654-6550-2
Contributo in Atti di Convegno (Proceeding) - Burrello, Alessio; Dequino, Alberto; Jahier Pagliari, Daniele; Conti, Franceso; ... (2021)
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference. In: 2021 IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2021, USA, 2021, pp. 1-6. ISBN: 978-1-6654-3922-0
Contributo in Atti di Convegno (Proceeding)