Track 11 Chairs

Charlotte FrenkelUniversité catholique de Louvain, Belgium

Gianvito UrgesePolitecnico 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