activity
20162022
most citedLow-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

175 citations · 343 across the 24 of their papers we have counts for

collaborators
Showing 2019Show all

11 papers · 1 filter

cs.ET2019

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…

physics.app-ph2019

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,…

cs.ET2019

Digital Biologically Plausible Implementation of Binarized Neural Networks with Differential Hafnium Oxide Resistive Memory Arrays

Tifenn Hirtzlin, Marc Bocquet, Bogdan Penkovsky +5

The brain performs intelligent tasks with extremely low energy consumption. This work takes inspiration from two strategies used by the brain to achieve this energy efficiency: the…

cond-mat.mes-hall2019

Voltage control of domain walls in magnetic nanowires for energy efficient neuromorphic devices

Md Ali Azam, Dhritiman Bhattacharya, Damien Querlioz +2

An energy-efficient voltage controlled domain wall device for implementing an artificial neuron and synapse is analyzed using micromagnetic modeling in the presence of room tempera…

cs.ET2019

Stochastic Computing for Hardware Implementation of Binarized Neural Networks

Tifenn Hirtzlin, Bogdan Penkovsky, Marc Bocquet +3

Binarized Neural Networks, a recently discovered class of neural networks with minimal memory requirements and no reliance on multiplication, are a fantastic opportunity for the re…

cs.LG20193 cited

Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input

Maxence Ernoult, Julie Grollier, Damien Querlioz +2

Equilibrium Propagation (EP) is a biologically inspired learning algorithm for convergent recurrent neural networks, i.e. RNNs that are fed by a static input x and settle to a stea…