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Innovating technologies for neuromorphic computing

Machine Learning and Neuroscience conference by The Royal Society

The Royal Society, London, United Kingdom · Hybrid
Abstracts due

About

This meeting examines how brain-inspired hardware could reduce AI’s growing energy requirements. Researchers connect materials and memory devices with circuits, systems and applications, including sparse computation and compute-in-memory architectures that reduce data movement. Two discussion days address fundamental advances and challenges; the final day considers translation and wider impacts. Programme participants include Steve Furber, Judith Driscoll and Tony Kenyon.

Topics

  • brain-inspired hardware
  • energy requirements
  • materials
  • memory devices
  • sparse computation
  • compute-in-memory architectures
  • data movement
  • circuits

People