3 citations · 3 across the 7 of their papers we have counts for
8 papers
Active Continual Learning with Metaplastic Binary Bayesian Neural Networks
Kellian Cottart, Théo Ballet, Djohan Bonnet +1
Always-on edge systems must keep learning as conditions change under tight compute budgets and must detect unreliable predictions. Bayesian binary neural networks are attractive in…
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…
Intrinsic Annealing in a Hybrid Memristor-Magnetic Tunnel Junction Ising Machine
Mohammed Akib Iftakher, Hugo Levices, Kamel-Eddine Harabi +10
Hardware implementations of the Ising model offer promising solutions to large-scale optimization tasks. In the literature, various nanodevices have been shown to emulate the spin…
Bayesian continual learning and forgetting in neural networks
Djohan Bonnet, Kellian Cottart, Tifenn Hirtzlin +4
Biological synapses effortlessly balance memory retention and flexibility, yet artificial neural networks still struggle with the extremes of catastrophic forgetting and catastroph…
Roadmap to Neuromorphic Computing with Emerging Technologies
Adnan Mehonic, Daniele Ielmini, Kaushik Roy +50
The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently util…
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…
Bayesian Metaplasticity from Synaptic Uncertainty
Djohan Bonnet, Tifenn Hirtzlin, Tarcisius Januel +3
Catastrophic forgetting remains a challenge for neural networks, especially in lifelong learning scenarios. In this study, we introduce MEtaplasticity from Synaptic Uncertainty (ME…