83 citations · 89 across the 8 of their papers we have counts for
14 papers
Experimental demonstration of Single-Level and Multi-Level-Cell RRAM-based In-Memory Computing with up to 16 parallel operations
E. Esmanhotto, T. Hirtzlin, N. Castellani +7
Crossbar arrays of resistive memories (RRAM) hold the promise of enabling In-Memory Computing (IMC), but essential challenges due to the impact of device imperfection and device en…
Model of the Weak Reset Process in HfOx Resistive Memory for Deep Learning Frameworks
Atreya Majumdar, Marc Bocquet, Tifenn Hirtzlin +6
The implementation of current deep learning training algorithms is power-hungry, owing to data transfer between memory and logic units. Oxide-based RRAMs are outstanding candidates…
Synaptic metaplasticity in binarized neural networks
Axel Laborieux, Maxence Ernoult, Tifenn Hirtzlin +1
Unlike the brain, artificial neural networks, including state-of-the-art deep neural networks for computer vision, are subject to "catastrophic forgetting": they rapidly forget the…
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…