Track 11 Chairs
Charlotte Frenkel, Université catholique de Louvain, Belgium
Gianvito Urgese, Politecnico di Torino, Italy
Topics of Interests
- 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).
- Multicore/Many-core Neuromorphic Platform
- Spiking Neural Network simulation on parallel neuromorphic platforms
- Embedded and Low power applications of Neuromorphic Platforms
