| Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses W Wang, G Pedretti, V Milo, R Carboni, A Calderoni, N Ramaswamy, ... Science advances 4 (9), eaat4752, 2018 | 267 | 2018 |
| Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM) synapses S Ambrogio, N Ciocchini, M Laudato, V Milo, A Pirovano, P Fantini, ... Frontiers in neuroscience 10, 56, 2016 | 256 | 2016 |
| Neuromorphic learning and recognition with one-transistor-one-resistor synapses and bistable metal oxide RRAM S Ambrogio, S Balatti, V Milo, R Carboni, ZQ Wang, A Calderoni, ... IEEE Transactions on Electron Devices 63 (4), 1508-1515, 2016 | 256 | 2016 |
| Multilevel HfO2-based RRAM devices for low-power neuromorphic networks V Milo, C Zambelli, P Olivo, E Pérez, M K Mahadevaiah, O G Ossorio, ... APL materials 7 (8), 2019 | 195 | 2019 |
| Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity G Pedretti, V Milo, S Ambrogio, R Carboni, S Bianchi, A Calderoni, ... Scientific reports 7 (1), 5288, 2017 | 183 | 2017 |
| Physical unbiased generation of random numbers with coupled resistive switching devices S Balatti, S Ambrogio, R Carboni, V Milo, Z Wang, A Calderoni, ... IEEE Transactions on Electron Devices 63 (5), 2029-2035, 2016 | 134 | 2016 |
| Memristive and CMOS devices for neuromorphic computing V Milo, G Malavena, C Monzio Compagnoni, D Ielmini Materials 13 (1), 166, 2020 | 131 | 2020 |
| Accurate program/verify schemes of resistive switching memory (RRAM) for in-memory neural network circuits V Milo, A Glukhov, E Pérez, C Zambelli, N Lepri, MK Mahadevaiah, ... IEEE Transactions on Electron Devices 68 (8), 3832-3837, 2021 | 89 | 2021 |
| Demonstration of hybrid CMOS/RRAM neural networks with spike time/rate-dependent plasticity V Milo, G Pedretti, R Carboni, A Calderoni, N Ramaswamy, S Ambrogio, ... 2016 IEEE International Electron Devices Meeting (IEDM), 16.8. 1-16.8. 4, 2016 | 86 | 2016 |
| Physics-based modeling approaches of resistive switching devices for memory and in-memory computing applications D Ielmini, V Milo Journal of Computational Electronics 16, 1121-1143, 2017 | 80 | 2017 |
| Stochastic learning in neuromorphic hardware via spike timing dependent plasticity with RRAM synapses G Pedretti, V Milo, S Ambrogio, R Carboni, S Bianchi, A Calderoni, ... IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8 (1 …, 2017 | 50 | 2017 |
| Analytical Modeling of Organic–Inorganic CH3NH3PbI3 Perovskite Resistive Switching and its Application for Neuromorphic Recognition Y Ren, V Milo, Z Wang, H Xu, D Ielmini, X Zhao, Y Liu Advanced Theory and Simulations 1 (4), 1700035, 2018 | 47 | 2018 |
| Novel RRAM-enabled 1T1R synapse capable of low-power STDP via burst-mode communication and real-time unsupervised machine learning S Ambrogio, S Balatti, V Milo, R Carboni, Z Wang, A Calderoni, ... 2016 IEEE Symposium on VLSI Technology, 1-2, 2016 | 44 | 2016 |
| A 4-transistors/1-resistor hybrid synapse based on resistive switching memory (RRAM) capable of spike-rate-dependent plasticity (SRDP) V Milo, G Pedretti, R Carboni, A Calderoni, N Ramaswamy, S Ambrogio, ... IEEE Transactions on Very Large Scale Integration (VLSI) Systems 26 (12 …, 2018 | 38 | 2018 |
| Computing of temporal information in spiking neural networks with ReRAM synapses W Wang, G Pedretti, V Milo, R Carboni, A Calderoni, N Ramaswamy, ... Faraday discussions 213, 453-469, 2019 | 37 | 2019 |
| A spiking recurrent neural network with phase-change memory neurons and synapses for the accelerated solution of constraint satisfaction problems G Pedretti, P Mannocci, S Hashemkhani, V Milo, O Melnic, E Chicca, ... IEEE Journal on Exploratory Solid-State Computational Devices and Circuits 6 …, 2020 | 36 | 2020 |
| Attractor networks and associative memories with STDP learning in RRAM synapses V Milo, D Ielmini, E Chicca 2017 IEEE International Electron Devices Meeting (IEDM), 11.2. 1-11.2. 4, 2017 | 35 | 2017 |
| Optimized programming algorithms for multilevel RRAM in hardware neural networks V Milo, F Anzalone, C Zambelli, E Pérez, MK Mahadevaiah, ÓG Ossorio, ... 2021 IEEE International Reliability Physics Symposium (IRPS), 1-6, 2021 | 34 | 2021 |
| Analytical modeling of current overshoot in oxide-based resistive switching memory (RRAM) S Ambrogio, V Milo, ZQ Wang, S Balatti, D Ielmini IEEE Electron Device Letters 37 (10), 1268-1271, 2016 | 33 | 2016 |
| Modeling-based design of brain-inspired spiking neural networks with RRAM learning synapses G Pedretti, S Bianchi, V Milo, A Calderoni, N Ramaswamy, D Ielmini 2017 IEEE International Electron Devices Meeting (IEDM), 28.1. 1-28.1. 4, 2017 | 30 | 2017 |
Daniele IelminiProfessor of ElectronicsPreverjeni e-poštni naslov na elet.polimi.it