Neuromorphic Engineering Algorithms for Edge ML and Spiking Neural Networks
Explore cutting-edge neuromorphic engineering concepts in this tinyML forum session focused on algorithms. Delve into hardware-friendly learning for edge ML with Ralph Etienne Cummings, investigate robustness and efficiency in neural systems using spike-based machine intelligence with Priya Panda, and learn about training spiking neural networks end-to-end using surrogate gradients from Friedemann Zenke. Discover how to enable neuromorphic learning machines through meta-learning with Emre Neftci. Gain insights into topics such as Batch Normalization Through Time (BNTT) for temporal learning, energy efficiency, and robustness in SNNs, Spike Activation Map (SAM) for interpretable SNNs, and bio-inspired homeostatic plasticity for optimal initialization. Understand the importance of end-to-end training in artificial neural networks and explore innovative solutions like surrogate gradients and Holomorphic Equilibrium Propagation for computing exact gradients through finite size oscillations. Read more