4 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…
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…
Hardware architecture and routing-aware training for optimal memory usage: a case study
Jimmy Weber, Theo Ballet, Melika Payvand
Efficient deployment of neural networks on resource-constrained hardware demands optimal use of on-chip memory. In event-based processors, this is particularly critical for routing…
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…