258 citations · 368 across the 20 of their papers we have counts for
6 papers · 1 filter
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…
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…
In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications
Bogdan Penkovsky, Marc Bocquet, Tifenn Hirtzlin +5
The advent of deep learning has considerably accelerated machine learning development. The deployment of deep neural networks at the edge is however limited by their high memory an…
Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse
Axel Laborieux, Marc Bocquet, Tifenn Hirtzlin +6
The design of systems implementing low precision neural networks with emerging memories such as resistive random access memory (RRAM) is a major lead for reducing the energy consum…
Spiking Neural Networks Hardware Implementations and Challenges: a Survey
Maxence Bouvier, Alexandre Valentian, Thomas Mesquida +4
Neuromorphic computing is henceforth a major research field for both academic and industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at bringing c…
In-situ learning harnessing intrinsic resistive memory variability through Markov Chain Monte Carlo Sampling
Thomas Dalgaty, Niccolo Castellani, Damien Querlioz +1
Resistive memory technologies promise to be a key component in unlocking the next generation of intelligent in-memory computing systems that can act and learn locally at the edge.…