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Valerio Milo
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2020 – today
- 2023
- [c11]Andrea Padovani, Paolo La Torraca, Jack Strand, Alexander L. Shluger, Valerio Milo, Luca Larcher:
Towards a Universal Model of Dielectric Breakdown. IRPS 2023: 1-8 - 2022
- [c10]Artem Glukhov, Valerio Milo, Andrea Baroni
, Nicola Lepri, Cristian Zambelli, Piero Olivo, Eduardo Pérez, Christian Wenger, Daniele Ielmini:
Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators. IRPS 2022: 3 - [c9]Andrea Padovani, Milan Pesic, Federico Nardi
Reliability of Non-Volatile Memory Devices for Neuromorphic Applications: A Modeling Perspective (Invited). IRPS 2022: 3 - [c8]Artem Glukhov, Nicola Lepri, Valerio Milo, Andrea Baroni
End-to-end modeling of variability-aware neural networks based on resistive-switching memory arrays. VLSI-SoC 2022: 1-5 - 2021
- [c7]Valerio Milo, Francesco Anzalone, Cristian Zambelli, Eduardo Pérez, Mamathamba Kalishettyhalli Mahadevaiah, Oscar Gonzalez Ossorio
Optimized programming algorithms for multilevel RRAM in hardware neural networks. IRPS 2021: 1-6 - 2020
- [c6]Giacomo Pedretti, Valerio Milo, S. Hashemkhani, Piergiulio Mannocci, Octavian Melnic, Elisabetta Chicca, Daniele Ielmini:
A Spiking Recurrent Neural Network with Phase Change Memory Synapses for Decision Making. ISCAS 2020: 1-5
2010 – 2019
- 2019
- [b1]Valerio Milo:
Modeling and simulation of spiking neural networks with resistive switching synapses. Polytechnic University of Milan, Italy, 2019 - [c5]Valerio Milo
Low-energy inference machine with multilevel HfO2 RRAM arrays. ESSDERC 2019: 174-177 - [c4]Erika Covi
A Volatile RRAM Synapse for Neuromorphic Computing. ICECS 2019: 903-906 - [p1]Daniele Ielmini, Valerio Milo
Brain-Inspired Memristive Neural Networks for Unsupervised Learning. Handbook of Memristor Networks 2019: 495-525 - 2018
- [j2]Giacomo Pedretti
Stochastic Learning in Neuromorphic Hardware via Spike Timing Dependent Plasticity With RRAM Synapses. IEEE J. Emerg. Sel. Topics Circuits Syst. 8(1): 77-85 (2018) - [j1]Valerio Milo
A 4-Transistors/1-Resistor Hybrid Synapse Based on Resistive Switching Memory (RRAM) Capable of Spike-Rate-Dependent Plasticity (SRDP). IEEE Trans. Very Large Scale Integr. Syst. 26(12): 2806-2815 (2018) - [c3]Valerio Milo
Brain-inspired recurrent neural network with plastic RRAM synapses. ISCAS 2018: 1-5 - [c2]Valerio Milo
Resistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks. ISCAS 2018: 1-5 - 2016
- [c1]Daniele Ielmini, Stefano Ambrogio, Valerio Milo
Neuromorphic computing with hybrid memristive/CMOS synapses for real-time learning. ISCAS 2016: 1386-1389
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