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Nicola Lepri
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2020 – today
- 2024
- [j1]Yang Li
, Wei Wang
Binary-Stochasticity-Enabled Highly Efficient Neuromorphic Deep Learning Achieves Better-than-Software Accuracy. Adv. Intell. Syst. 6(1) (2024) - 2023
- [c6]Xabier Iturbe, Nassim Abderrahmane, Jaume Abella
NimbleAI: Towards Neuromorphic Sensing-Processing 3D-integrated Chips. DATE 2023: 1-6 - [c5]Nicola Lepri, P. Gibertini, Piergiulio Mannocci, A. Pirovano, I. Tortorelli, Paolo Fantini, Daniele Ielmini:
In-memory neural network accelerator based on phase change memory (PCM) with one-selector/one-resistor (1S1R) structure operated in the subthreshold regime. IMW 2023: 1-4 - [c4]L. Cattaneo
Enhancing reliability of a strong physical unclonable function (PUF) solution based on virgin-state phase change memory (PCM). IRPS 2023: 1-6 - [i1]Yang Li, Wei Wang, Ming Wang, Chunmeng Dou, Zhengyu Ma, Huihui Zhou, Peng Zhang, Nicola Lepri, Xumeng Zhang, Qing Luo, Xiaoxin Xu, Guanhua Yang, Feng Zhang, Ling Li, Daniele Ielmini, Ming Liu:
Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy. CoRR abs/2304.12866 (2023) - 2022
- [c3]Artem Glukhov, Valerio Milo, Andrea Baroni
Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators. IRPS 2022: 3 - [c2]Nicola Lepri, Artem Glukhov, Daniele Ielmini:
Mitigating read-program variation and IR drop by circuit architecture in RRAM-based neural network accelerators. IRPS 2022: 3 - [c1]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
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