Davide Pilati

Ph.D. candidate in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 39th cycle (2023-2026)
Department of Applied Science and Technology (DISAT)



Research topic

Unconventional computing with neuromorphic nanowire networks


Research interests

Big Data, Machine Learning, Neural Networks and Data Science
Electronic devices: modeling and characterization
Micro- and nanotechnologies, devices, systems and applications


Davide is a Ph.D. student in the field of electronics and nanotechnology, with a particular focus on nanocomputing and its applications in reservoir computing and neuromorphic nanowire networks. His academic journey began with an undergraduate degree in electronics engineering, where a course in digital electronics piqued his interest in neuromorphic computing. This initial intrigue evolved into a profound commitment to exploring the capabilities of nanowire networks in computational systems.

For his thesis, Davide developed and implemented a novel code to adapt a custom-designed electronic measurement board for use with nanowire networks. The heart of his thesis involved the multielectrode characterization of nanowire networks, revealing asymmetrical turn-on paths and tentative turn-ons. These findings provided new insights into the behavior of these networks and opened up avenues for further research.

One of Davide's most notable contributions to the field is the setup development for multielectrode characterization of these complex networks, which can be exploited for studying the emergent behavior of the system, as well as performing complex computational tasks.

As he looks towards his PhD, Davide is driven by ambitious goals. He aims to explore the potential of memristive nanowire networks further, with the hope of integrating this technology into systems for accelerated computation. Additionally, he is curious about the potential to exploit network nonlinearities to simplify computing architectures, potentially bypassing the need for a readout layer.


Other activities and projects related to research

The main objective of the PhD work is the experimental implementation of unconventional computing paradigms and neuromorphic functionalities in multiterminal devices based on memristive nanowire networks.The aim is the development of a hardware computing architectures able to solve a wide range of computing tasks such as pattern recognition and time series prediction with reduced training cost and energy consumption.