7 citations · 7 across the 6 of their papers we have counts for
6 papers
Uncertainty-triggered wake-up enables energy-efficient, error-resilient edge AI with memristor front ends
Théo Ballet, Aymen Romdhane, Bruno Lovison-Franco +11
Memristor computing offers a route to low-energy edge AI, but device variability, sensitivity to operating conditions, and system-integration challenges can hinder deployment. Here…
Forward-only learning in memristor arrays with month-scale stability
Adrien Renaudineau, Mamadou Hawa Diallo, Théo Dupuis +12
Turning memristor arrays from efficient inference engines into systems capable of on-chip learning has proved difficult. Weight updates have a high energy cost and cause device wea…
Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses
Nikhil Garg, Ismael Balafrej, Joao Henrique Quintino Palhares +11
The deployment of AI on edge computing devices faces significant challenges related to energy consumption and functionality. These devices could greatly benefit from brain-inspired…
Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits
Nikhil Garg, Davide Florini, Patrick Dufour +8
Heterogeneous systems with analog CMOS circuits integrated with nanoscale memristive devices enable efficient deployment of neural networks on neuromorphic hardware. CMOS Neuron wi…
The Logarithmic Memristor-Based Bayesian Machine
Clément Turck, Kamel-Eddine Harabi, Adrien Pontlevy +9
The demand for explainable and energy-efficient artificial intelligence (AI) systems for edge computing has led to significant interest in electronic systems dedicated to Bayesian…
Ultra-High-density 3D vertical RRAM with stacked JunctionLess nanowires for In-Memory-Computing applications
M. Ezzadeen, D. Bosch, B. Giraud +6
The Von-Neumann bottleneck is a clear limitation for data-intensive applications, bringing in-memory computing (IMC) solutions to the fore. Since large data sets are usually stored…