Track: Embedded Neuromorphic Computing Systems

Track Chairs

Charlotte Frenkel (UCLouvain, Belgium), Gianvito Urgese (PoliTO, Italy)

Neuromorphic computing is inspired by the neurobiological system and opens up computing possibilities beyond traditional Von-Neumann systems. It aims to explore novel opportunities for low power processing of sensory data for cognitive applications using spiking neural networks (SNNs).  
The goal of this track is to bring together leading researchers in neuromorphic computing systems to present new research and provide a forum to publish work in this area. The focus will be on architectures, models, and applications of embedded neuromorphic computing systems. The topics of interest of the track include, but are not limited to:

  • 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).

Former Chairs

  • Charlotte Frenkel, Université catholique de Louvain, Belgium , MCSoC 2019
  • Andrea Acquaviva, Politecnico di Torino, Italy , MCSoC 2019
  • Salvatore Vitabile, University of Palermo, Italy , MCSoC 2018