582 citations · 768 across the 23 of their papers we have counts for
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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…
Synaptic Metaplasticity in Binarized Neural Networks
Axel Laborieux, Maxence Ernoult, Tifenn Hirtzlin +1
While deep neural networks have surpassed human performance in multiple situations, they are prone to catastrophic forgetting: upon training a new task, they rapidly forget previou…