175 citations · 180 across the 4 of their papers we have counts for
10 papers
Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction
Tifenn Hirtzlin, Bogdan Penkovsky, Jacques-Olivier Klein +5
One of the most exciting applications of Spin Torque Magnetoresistive Random Access Memory (ST-MRAM) is the in-memory implementation of deep neural networks, which could allow impr…
Designing large arrays of interacting spin-torque nano-oscillators for microwave information processing
Philippe Talatchian, Miguel Romera, Flavio Abreu Araujo +6
Arrays of spin-torque nano-oscillators are promising for broadband microwave signal detection and processing, as well as for neuromorphic computing. In many of these applications,…
Microwave neural processing and broadcasting with spintronic nano-oscillators
P. Talatchian, M. Romera, S. Tsunegi +14
Can we build small neuromorphic chips capable of training deep networks with billions of parameters? This challenge requires hardware neurons and synapses with nanometric dimension…
Use of Magnetoresistive Random-Access Memory as Approximate Memory for Training Neural Networks
Nicolas Locatelli, Adrien F. Vincent, Damien Querlioz
Hardware neural networks that implement synaptic weights with embedded non-volatile memory, such as spin torque memory (ST-MRAM), are a major lead for low energy artificial intelli…
Nano-oscillator-based classification with a machine learning-compatible architecture
Damir Vodenicarevic, Nicolas Locatelli, Julie Grollier +1
Pattern classification architectures leveraging the physics of coupled nano-oscillators have been demonstrated as promising alternative computing approaches, but lack effective lea…
Circuit-Level Evaluation of the Generation of Truly Random Bits with Superparamagnetic Tunnel Junctions
Damir Vodenicarevic, Nicolas Locatelli, Alice Mizrahi +4
Many emerging alternative models of computation require massive numbers of random bits, but their generation at low energy is currently a challenge. The superparamagnetic tunnel ju…