Accelerating Inference and Training at the Edge

Join us for a talk by Maxence Ernoult, Research Scientist at Rain, on accelerating inference and training at the edge.

Accelerating Inference and Training at the Edge
  • Maxence Ernoult
  • March 5, 2024

Maxence will present us Rain’s vision and technological roadmap to build hardware optimized for inference and training at the edge including both the hardware and algorithm aspects with an emphasis on why physical and mathematical principles matter more to him than biological inspiration.

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Upcoming Workshops

Advances in Neuromorphic Visual Place Recognition
Tobias Fischer
2024, Apr 18    23:00 - 00:30 CET
Accelerating Inference and Training at the Edge

About the Speaker

Maxence Ernoult graduated from Ecole Polytechnique and the University of Cambridge in 2016, specializing in applied mathematics and theoretical physics. His PhD research was conducted in neuromorphic computing at Sorbonne University, in collaboration with Mila. During this time, he specialized in developing hardware-friendly alternatives to backpropagation and played a significant role in scaling up several of these alternatives, including Equilibrium Propagation and Difference Target Propagation. This work was undertaken alongside notable figures such as Ben Scellier, Blake Richards, and Yoshua Bengio. In 2021, Maxence joined IBM Research, focusing on AI safety. Subsequently, in 2022, he began a new position at Rain.

Hands-On with Sinabs and Speck

Hands-On with Sinabs and Speck

  • Gregor Lenz
  • 2023, April 4

Join Gregor Lenz for an engaging hands-on session featuring Sinabs and Speck. Explore the world of neuromorphic engineering and spike-based machine learning.

IBM NorthPole - Neural inference at the frontier of energy, space, and time

IBM NorthPole - Neural inference at the frontier of energy, space, and time

  • Carlos Ortega-Otero
  • 2024, January 25

NorthPole outperforms all prevalent architectures, even those that use more-advanced technology processes.

The ELM Neuron: An Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks

The ELM Neuron: An Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks

  • Aaron Spieler
  • 2024, February 27

Aaron tells us about the Expressive Leaky Memory (ELM) neuron model, a biologically inspired phenomenological model of a cortical neuron.