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Stefano Ambrogio

Stefano Ambrogio

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
37
Citations
6207
World Ranking
5098
National Ranking
1771

Overview

Stefano Ambrogio is affiliated with Meta for Business in the United States. Their research primarily spans the fields of Engineering and Computer Science, with a focus on Electrical and Electronic Engineering, Artificial Intelligence, Materials Chemistry, Computer Vision and Pattern Recognition, and Hardware and Architecture.

Their work covers several topics including Advanced Memory and Neural Computing, Ferroelectric and Negative Capacitance Devices, Neural Networks and Reservoir Computing, Advanced Neural Network Applications, Machine Learning in Materials Science, Phase-change Materials and Chalcogenides, and Quantum Computing Algorithms and Architecture.

Frequent coauthors in their research include Charles Mackin, Pritish Narayanan, Hsinyu Tsai, Geoffrey W. Burr, and Alexander Friz.

Stefano Ambrogio has published extensively in venues such as IEEE Transactions on Electron Devices, arXiv (Cornell University), Nature Communications, Nature, and APL Materials.

Recent papers authored or coauthored by Stefano Ambrogio include the following:

  • "An analog-AI chip for energy-efficient speech recognition and transcription" (2023, Nature)
  • "Roadmap to neuromorphic computing with emerging technologies" (2024, APL Materials)
  • "Optimised weight programming for analogue memory-based deep neural networks" (2022, Nature Communications)
  • "Noise-Resilient DNN: Tolerating Noise in PCM-Based AI Accelerators via Noise-Aware Training" (2021, IEEE Transactions on Electron Devices)
  • "Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices" (2021, Frontiers in Computational Neuroscience)

Best Publications

  • Equivalent-accuracy accelerated neural-network training using analogue memory

    Stefano Ambrogio;Pritish Narayanan;Hsinyu Tsai;Robert M. Shelby

  • Recommended Methods to Study Resistive Switching Devices

    Mario Lanza;H.-S. Philip Wong;Eric Pop;Daniele Ielmini

  • Emerging neuromorphic devices.

    Daniele Ielmini;Stefano Ambrogio

  • Statistical Fluctuations in HfO x Resistive-Switching Memory: Part I - Set/Reset Variability

    Stefano Ambrogio;Simone Balatti;Antonio Cubeta;Alessandro Calderoni

  • Neuromorphic Learning and Recognition With One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM

    Stefano Ambrogio;Simone Balatti;Valerio Milo;Roberto Carboni

  • Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity

    G. Pedretti;V. Milo;S. Ambrogio;R. Carboni

  • Analytical Modeling of Oxide-Based Bipolar Resistive Memories and Complementary Resistive Switches

    Stefano Ambrogio;Simone Balatti;David C. Gilmer;Daniele Ielmini

  • True Random Number Generation by Variability of Resistive Switching in Oxide-Based Devices

    Simone Balatti;Stefano Ambrogio;Zhongqiang Wang;Daniele Ielmini

  • Statistical Fluctuations in HfO x Resistive-Switching Memory: Part II—Random Telegraph Noise

    Stefano Ambrogio;Simone Balatti;Antonio Cubeta;Alessandro Calderoni

  • Spike-timing dependent plasticity in a transistor-selected resistive switching memory.

    S Ambrogio;S Balatti;F Nardi;S Facchinetti

  • Physical Unbiased Generation of Random Numbers With Coupled Resistive Switching Devices

    Simone Balatti;Stefano Ambrogio;Roberto Carboni;Valerio Milo

  • Voltage-Controlled Cycling Endurance of HfO x -Based Resistive-Switching Memory

    Simone Balatti;Stefano Ambrogio;Zhongqiang Wang;Scott Sills

  • Ultrafast valley relaxation dynamics in monolayer MoS 2 probed by nonequilibrium optical techniques

    S. Dal Conte;F. Bottegoni;E. A. A. Pogna;D. De Fazio

  • Impact of the Mechanical Stress on Switching Characteristics of Electrochemical Resistive Memory

    Stefano Ambrogio;Simone Balatti;Seol Choi;Daniele Ielmini

  • A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems

    Zhongqiang Wang;Stefano Ambrogio;Simone Balatti;Daniele Ielmini

  • Normally-off Logic Based on Resistive Switches—Part I: Logic Gates

    Simone Balatti;Stefano Ambrogio;Daniele Ielmini

  • Set Variability and Failure Induced by Complementary Switching in Bipolar RRAM

    S. Balatti;S. Ambrogio;D. C. Gilmer;D. Ielmini

  • Understanding switching variability and random telegraph noise in resistive RAM

    S. Ambrogio;S. Balatti;A. Cubeta;A. Calderoni

  • Demonstration of hybrid CMOS/RRAM neural networks with spike time/rate-dependent plasticity

    V. Milo;G. Pedretti;R. Carboni;A. Calderoni

  • Noise-Induced Resistance Broadening in Resistive Switching Memory—Part II: Array Statistics

    Stefano Ambrogio;Simone Balatti;Vincent McCaffrey;Daniel C. Wang

Frequent Co-Authors

Daniele Ielmini
Daniele Ielmini Polytechnic University of Milan
Andrea C. Ferrari
Andrea C. Ferrari University of Cambridge
Alessandro S. Spinelli
Alessandro S. Spinelli Polytechnic University of Milan
Abu Sebastian
Abu Sebastian IBM Research - Zurich
Vijay Narayanan
Vijay Narayanan IBM (United States)
Ilia Valov
Ilia Valov RWTH Aachen University
Eric Pop
Eric Pop Stanford University
Max C. Lemme
Max C. Lemme RWTH Aachen University
Hangbing Lv
Hangbing Lv Chinese Academy of Sciences
Deji Akinwande
Deji Akinwande The University of Texas at Austin

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