Training spiking neural networks end-to-end with surrogate gradients

Towards “Greener” AI on the Edge: Energy-Efficient Neuromorphic Learning and Inference

Tiny spiking neural networks for sub-milliwatt AI at the sensor-edge

The role of neuromorphic analog computing in solutions for Industry 4.0

Event driven signal processing

Hardware Friendly Learning for Edge ML

Fully Spike-based Architecture with Front-end Dynamic Vision Sensor and Back-end Spiking Neural Network

The SpiNNaker Project

Neuromorphic intelligence and learning in robotics

Edge ML Developer Day in Kenya