Fabrizio Ottati

Dottorato in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 36^o ciclo (2020-2023)

Dottorato concluso nel 2024

Profilo

Argomento di ricerca

Efficient hardware architectures for brain-inspired computing

Interessi di ricerca

Big Data, Machine Learning, Neural Networks and Data Science

Micro- and nanotechnologies, devices, systems and applications

VLSI theory, design and applications

Biografia

Fabrizio Ottati received the Bachelor of Science (2017) and Master of Science (2020) degrees in Electronic Engineering at Politecnico di Torino, where he has joined the doctoral program in Electrical, Electronics and Communications Engineering, in 2020.
His main research activities focus on innovative digital architectures for the brain-inspired computing paradigm. In particular, he is interested in the Hyperdimensional Computing, Spiking Neural Networks, silicon retinas and neuromorphic computing. He is deeply involved with the open-source neuromorphic community, collaborating with researchers from all over the world and maintaining many open-source projects.

Didattica

Insegnamenti

Corso di laurea magistrale

Sistemi digitali integrati. A.A. 2021/22, INGEGNERIA ELETTRONICA (ELECTRONIC ENGINEERING). Collaboratore del corso
Sistemi digitali integrati. A.A. 2022/23, INGEGNERIA ELETTRONICA (ELECTRONIC ENGINEERING). Collaboratore del corso
Workshop "Innovative systems". A.A. 2020/21, INGEGNERIA ELETTRONICA (ELECTRONIC ENGINEERING). Collaboratore del corso
Electronic Systems for Sensor Acquisition. A.A. 2021/22, MECHATRONIC ENGINEERING (INGEGNERIA MECCATRONICA). Collaboratore del corso
Electronic Systems for Sensor Acquisition. A.A. 2022/23, MECHATRONIC ENGINEERING (INGEGNERIA MECCATRONICA). Collaboratore del corso

MostraNascondi A.A. passati

Pubblicazioni

Pubblicazioni durante il dottorato Vedi tutte le pubblicazioni su Porto@Iris

Ottati, Fabrizio (2024)
Efficient Deep Learning Inference: A Digital Hardware Perspective - Evaluating and improving performance and efficiency of artificial and spiking neural networks hardware accelerators. relatore: LAVAGNO, Luciano; CASU, MARIO ROBERTO; , 36. XXXVI Ciclo, P.: 72
Doctoral Thesis
Ottati, Fabrizio; Gao, Chang; Chen, Qinyu; Brignone, Giovanni; Casu, Mario Roberto; ... (2023)
To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration. In: IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, vol. 13, pp. 1015-1025. ISSN 2156-3365
Contributo su Rivista
Ottati, Fabrizio; Turvani, Giovanna; Masera, Guido; Vacca, Marco (2021)
Custom Memory Design for Logic-in-Memory: Drawbacks and Improvements over Conventional Memories. In: ELECTRONICS, vol. 10. ISSN 2079-9292
Contributo su Rivista