Stefano Ambrogio

发表

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

Alessandro Calderoni, Daniele Ielmini, Valerio Milo, 2018, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses

Paolo Fantini, Daniele Ielmini, Valerio Milo, 2016, Front. Neurosci..

Accelerating Deep Neural Networks with Analog Memory Devices

Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, 2020, 2020 IEEE International Memory Workshop (IMW).

Training fully connected networks with resistive memories: impact of device failures.

Pritish Narayanan, Stefano Ambrogio, Hsinyu Tsai, 2019, Faraday discussions.

Accelerating Deep Neural Networks with Analog Memory Devices

Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, 2020, 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS).

AI hardware acceleration with analog memory: Microarchitectures for low energy at high speed

Pritish Narayanan, Scott C. Lewis, Geoffrey W. Burr, 2019, IBM J. Res. Dev..

Accelerating Deep Neural Networks with Analog Memory Devices

Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, 2019, 2019 China Semiconductor Technology International Conference (CSTIC).

Equivalent-accuracy accelerated neural-network training using analogue memory

Pritish Narayanan, Stefano Ambrogio, Irem Boybat, 2018, Nature.

Analog acceleration of deep learning using phase-change memory

Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, 2020 .

Perspective on training fully connected networks with resistive memories: Device requirements for multiple conductances of varying significance

Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, 2018 .

Neuromorphic Technologies for Next-Generation Cognitive Computing

Pritish Narayanan, Stefano Ambrogio, Robert M. Shelby, 2017, 2017 IEEE International Memory Workshop (IMW).

Inference of Deep Neural Networks with Analog Memory Devices

Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, 2020, 2020 International Symposium on VLSI Technology, Systems and Applications (VLSI-TSA).

Unsupervised Learning to Overcome Catastrophic Forgetting in Neural Networks

Daniele Ielmini, Giacomo Pedretti, Stefano Ambrogio, 2019, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits.

Neuro-Inspired Computing: From Resistive Memory to Optics

Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, 2019, 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC).

Neuromorphic Computing with Phase Change, Device Reliability, and Variability Challenges

Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, 2020, 2020 IEEE International Reliability Physics Symposium (IRPS).

Analog-to-Digital Conversion With Reconfigurable Function Mapping for Neural Networks Activation Function Acceleration

Pritish Narayanan, Koji Ishibashi, Stefano Ambrogio, 2019, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

Energy-efficient continual learning in hybrid supervised-unsupervised neural networks with PCM synapses

Daniele Ielmini, Giacomo Pedretti, Stefano Ambrogio, 2019, 2019 Symposium on VLSI Technology.

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

Simone Balatti, Daniele Ielmini, Stefano Ambrogio, 2015, Front. Neurosci..

Optimization of Analog Accelerators for Deep Neural Networks Inference

Pritish Narayanan, Geoffrey W. Burr, An Chen, 2020, ISCAS.

Enabling High-Performance DNN Inference Accelerators Using Non-Volatile Analog Memory (Invited)

Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, 2020, 2020 4th IEEE Electron Devices Technology & Manufacturing Conference (EDTM).

Neuromorphic technologies for next-generation cognitive computing

Pritish Narayanan, Scott C. Lewis, Stefano Ambrogio, 2017, 2017 IEEE Electron Devices Technology and Manufacturing Conference (EDTM).

Neuromorphic computing with resistive switching memory devices

Daniele Ielmini, Stefano Ambrogio, S. Ambrogio, 2019, Advances in Non-Volatile Memory and Storage Technology.