Dynamical Neuromorphic Systems

  • Online

In this talk, Dr. Julie Grollier aims to show that the dynamical properties of emerging nanodevices can accelerate the development of smart, and environmentally friendly chips that inherently learn through their physics.

The goal of neuromorphic computing is to draw inspiration from the architecture of the brain to build low-power circuits for artificial intelligence. Dr. Grollier will first give a brief overview of the state of the art of neuromorphic computing, highlighting the opportunities offered by emerging nanodevices in this field, and the associated challenges. Dr. Grollier will then show that the intrinsic dynamical properties of these nanodevices can be exploited at the device and algorithmic level to assemble systems that infer and learn though their physics. Dr. Grollier will illustrate these possibilities with examples from our work on spintronic neural networks that communicate and compute through their microwave oscillations, and on an algorithm called Equilibrium Propagation that minimizes both the error and energy of a dynamical system.

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Dr. Julie Grollier


Dr. Julie Grollier is a senior researcher in the CNRS/Thales laboratory south of Paris, where she leads the team on nanodevices for neuromorphic computing. Her work is interdisciplinary, from the physics of spintronic and resistive switching materials to the development of learning algorithms for Artificial Intelligence.

Publication Year: 2021

Dynamical Neuromorphic Systems
  • Course Provider: IEEE Brain
  • Course Number: BRAINWEB0012
  • Duration (Hours): 1
  • Credits: None