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

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Showing 2020Show all

16 papers · 1 filter

cond-mat.dis-nn202034 cited

Radio-Frequency Multiply-And-Accumulate Operations with Spintronic Synapses

N. Leroux, D. Marković, E. Martin +4

Exploiting the physics of nanoelectronic devices is a major lead for implementing compact, fast, and energy efficient artificial intelligence. In this work, we propose an original…

cond-mat.mes-hall2020

Tunable stochasticity in an artificial spin network

Dédalo Sanz-Hernández, Maryam Massouras, Nicolas Reyren +9

Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a larg…

cs.NE2020

EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations

Erwann Martin, Maxence Ernoult, Jérémie Laydevant +4

Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge.…

cs.ET2020

Embracing the Unreliability of Memory Devices for Neuromorphic Computing

Marc Bocquet, Tifenn Hirtzlin, Jacques-Olivier Klein +4

The emergence of resistive non-volatile memories opens the way to highly energy-efficient computation near- or in-memory. However, this type of computation is not compatible with c…

physics.app-ph20202 cited

Spintronics for neuromorphic computing

J. Grollier, D. Querlioz, K. Y. Camsari +3

Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches t…

cs.ET2020

Implementation of Ternary Weights with Resistive RAM Using a Single Sense Operation per Synapse

Axel Laborieux, Marc Bocquet, Tifenn Hirtzlin +5

The design of systems implementing low precision neural networks with emerging memories such as resistive random access memory (RRAM) is a significant lead for reducing the energy…