258 citations · 368 across the 20 of their papers we have counts for
5 papers · 1 filter
A Memristor-Based Bayesian Machine
Kamel-Eddine Harabi, Tifenn Hirtzlin, Clément Turck +7
In recent years, a considerable research effort has shown the energy benefits of implementing neural networks with memristors or other emerging memory technologies. However, for ex…
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…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
Experimental Body-input Three-stage DC offset Calibration Scheme for Memristive Crossbar
Charanraj Mohan, L. A. Camuñas-Mesa, Elisa Vianello +4
Reading several ReRAMs simultaneously in a neuromorphic circuit increases power consumption and limits scalability. Applying small inference read pulses is a vain attempt when offs…
PCM-trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials
Yigit Demirag, Filippo Moro, Thomas Dalgaty +5
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the…