Valerio Milo

According to our database, Valerio Milo authored at least 15 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Towards a Universal Model of Dielectric Breakdown.

Andrea Padovani, Paolo La Torraca, Jack Strand, Alexander L. Shluger, Valerio Milo, Luca Larcher

Proceedings of the IEEE International Reliability Physics Symposium, 2023

2022

End-to-end modeling of variability-aware neural networks based on resistive-switching memory arrays.

Artem Glukhov, Nicola Lepri, Valerio Milo, Andrea Baroni, Cristian Zambelli, Piero Olivo, Eduardo Pérez, Christian Wenger, Daniele Ielmini

Proceedings of the 30th IFIP/IEEE 30th International Conference on Very Large Scale Integration, 2022

Reliability of Non-Volatile Memory Devices for Neuromorphic Applications: A Modeling Perspective (Invited).

Andrea Padovani, Milan Pesic, Federico Nardi, Valerio Milo, Luca Larcher, Mondol Anik Kumar, Zunaid Baten

Proceedings of the IEEE International Reliability Physics Symposium, 2022

Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators.

Artem Glukhov, Valerio Milo, Andrea Baroni, Nicola Lepri, Cristian Zambelli, Piero Olivo, Eduardo Pérez, Christian Wenger, Daniele Ielmini

Proceedings of the IEEE International Reliability Physics Symposium, 2022

2021

Optimized programming algorithms for multilevel RRAM in hardware neural networks.

Valerio Milo, Francesco Anzalone, Cristian Zambelli, Eduardo Pérez, Mamathamba Kalishettyhalli Mahadevaiah, Oscar Gonzalez Ossorio, Piero Olivo, Christian Wenger, Daniele Ielmini

Proceedings of the IEEE International Reliability Physics Symposium, 2021

2020

A Spiking Recurrent Neural Network with Phase Change Memory Synapses for Decision Making.

Giacomo Pedretti, Valerio Milo, S. Hashemkhani, Piergiulio Mannocci, Octavian Melnic, Elisabetta Chicca, Daniele Ielmini

Proceedings of the IEEE International Symposium on Circuits and Systems, 2020

2019

Modeling and simulation of spiking neural networks with resistive switching synapses.

Valerio Milo

PhD thesis, 2019

A Volatile RRAM Synapse for Neuromorphic Computing.

Erika Covi, Daniele Ielmini, Y.-H. Lin, Wei Wang, Tommaso Stecconi, Valerio Milo, Alessandro Bricalli, Elia Ambrosi, Giacomo Pedretti, Tseung-Yuen Tseng

Proceedings of the 26th IEEE International Conference on Electronics, Circuits and Systems, 2019

Low-energy inference machine with multilevel HfO2 RRAM arrays.

Valerio Milo, Cristian Zambelli, Piero Olivo, Eduardo Pérez, Oscar Gonzalez Ossorio, Christian Wenger, Daniele Ielmini

Proceedings of the 49th European Solid-State Device Research Conference, 2019

Brain-Inspired Memristive Neural Networks for Unsupervised Learning.

Daniele Ielmini, Valerio Milo

Proceedings of the Handbook of Memristor Networks., 2019

2018

A 4-Transistors/1-Resistor Hybrid Synapse Based on Resistive Switching Memory (RRAM) Capable of Spike-Rate-Dependent Plasticity (SRDP).

Valerio Milo, Giacomo Pedretti, Roberto Carboni, Alessandro Calderoni, Nirmal Ramaswamy, Stefano Ambrogio, Daniele Ielmini

IEEE Trans. Very Large Scale Integr. Syst., 2018

Stochastic Learning in Neuromorphic Hardware via Spike Timing Dependent Plasticity With RRAM Synapses.

Giacomo Pedretti, Valerio Milo, Stefano Ambrogio, Roberto Carboni, Stefano Bianchi, Alessandro Calderoni, Nirmal Ramaswamy, Alessandro S. Spinelli, Daniele Ielmini

IEEE J. Emerg. Sel. Topics Circuits Syst., 2018

Resistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks.

Valerio Milo, Giacomo Pedretti, Mario Laudato, Alessandro Bricalli, Elia Ambrosi, Stefano Bianchi, Elisabetta Chicca, Daniele Ielmini

Proceedings of the IEEE International Symposium on Circuits and Systems, 2018

Brain-inspired recurrent neural network with plastic RRAM synapses.

Valerio Milo, Elisabetta Chicca, Daniele Ielmini

Proceedings of the IEEE International Symposium on Circuits and Systems, 2018

2016

Neuromorphic computing with hybrid memristive/CMOS synapses for real-time learning.

Daniele Ielmini, Stefano Ambrogio, Valerio Milo, Simone Balatti, Zhongqiang Wang

Proceedings of the IEEE International Symposium on Circuits and Systems, 2016