Gregor Lenz

SynSense

  • Company focuses on ultra-low-power inference on the edge
  • Hardware is digital asynchronous for Speck and digital synchronous for
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What I do

  • Designing the machine learning pipeline to train our models
     
  • Focus on engineering the tools for gradient-based optimization of SNNs, data wrangling
     
  • Establish the use of good practices to make training and deployment faster

Research

  • Training SNNs for better performance through data augmentation
     
  • Architectural exploration such as recurrence in the network, spike frequency adaptation
     
  • How to save energy, fewer spikes is the goal

Research

  • Training Spiking Neural Networks Using Lessons from Deep Learning: Eshragian, Neftci, Wang, Lenz
     
  • Adversarial Attacks on Spiking Convolutional Networks: Büchel, Lenz, Sheik, Sorbaro
     
  • EXODUS: Stable and Efficient Training of Spiking Neural Networks: Bauer, Lenz, Haghighatshoar, Sheik
     
  • under submission: Ultra-low-power image classification on neuromorphic hardware

Patents

  • Converting an ANN to an SNN using temporal coding and a single spike per neuron
     
  • A method to normalise ANN activation which scales to deeper layers

Outreach

  • CapoCaccia workshop: presented our chips, gave tutorials and demos
     
  • AMLD: a full-day workshop with our hardware, outreach to new students
     
  • Open Neuromorphic: Platform for neuromorphic open source code and hardware
     
  • NeuroBench: benchmarking and metrics for neuromorphic computing

Summary

  • Leading role in the algorithms vision team
     
  • Strong engineering background
     
  • 3 papers published in the last year, 1 under submission
     
  • 2 patents submitted
     
  • Involved in numerous community efforts
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