Track 11 Chairs

Charlotte Frenkel, Université catholique de Louvain, Belgium

 

 

 

 

Andrea Acquaviva, Politecnico di Torino, Italy

 

 

Topics of Interest

• Neuromorphic hardware
• Algorithm, and architecture co-optimization for efficient machine-learning hardware design
• Models for neurons and synapses
• Spiking neuro-inspired architectures building blocks
• Reliable communication networks for neuro-inspired chips/systems
• Conventional hardware (i.e. VLSI, FPGAs) and innovative hardware (i.e., memristor) implementation of Neuromorphic systems
• Reconfigurability and adaptability methods
• Deep learning models
• New applications of on-chip learning (i.e., mobile devices, IoT, Edge).