11 citations · 32 across the 6 of their papers we have counts for
6 papers · 1 filter
Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing
Anastasios Petropoulos, Irem Boybat, Manuel Le Gallo +3
In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory…
Mixed-precision deep learning based on computational memory
S. R. Nandakumar, Manuel Le Gallo, Christophe Piveteau +11
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition…
Accurate deep neural network inference using computational phase-change memory
Vinay Joshi, Manuel Le Gallo, Simon Haefeli +7
In-memory computing is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. Crossbar arrays of resistive memory devices can be used to…
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
S. R. Nandakumar, Irem Boybat, Manuel Le Gallo +3
Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The…
Low-Power Neuromorphic Hardware for Signal Processing Applications
Bipin Rajendran, Abu Sebastian, Michael Schmuker +2
Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting ma…
Mixed-precision training of deep neural networks using computational memory
Nandakumar S. R., Manuel Le Gallo, Irem Boybat +3
Deep neural networks have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image and speec…