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).