Join the EDGE AI FOUNDATION and our Neuromorphic Working Group for a special livestream exploring how neuromorphic computing, event-based sensing, spiking neural networks, and sparsity-aware architectures are enabling a new generation of efficient and intelligent Edge AI systems.
Bringing together experts from BrainChip, fortiss, Politecnico di Torino, Sony Advanced Visual Sensing, Fraunhofer, CSEM, and DTU, the program will move from emerging research to practical applications and deployment strategies.
Topics will include conditional sparsity and the future of neuromorphic hardware, adaptive event-based perception, continual and federated learning for industrial robotics, privacy-preserving fall detection using event-based vision and neuromorphic processors, spiking neural networks for real-time control, and new approaches to measuring computational efficiency in sparse and spiking neural networks.
Featured presentations include:
From CNNs to Conditional Sparsity: A Deployment Strategy for the Next Decade of Neuromorphic Hardware — BrainChip
Adaptive Closed-Loop Control of Temporal Integration in Event-Based Embedded Perception — fortiss
Continual and Federated Learning of Neuromorphic Gesture Interfaces for Industrial Welding Robots — Politecnico di Torino
Privacy-Preserving Fall Detection at the Edge Using Sony IMX636 Event-Based Vision Sensor and Intel Loihi 2 Neuromorphic Processor — Sony Advanced Visual Sensing
Spiking Neural Networks for Low-Latency, High-Reliability Feedback Control in Power Conversion Systems: The EdgeAI Approach — Fraunhofer Institute for Integrated Circuits / Technische Hochschule Nürnberg
EFLOP: A Sparsity-Aware Metric for Evaluating Computational Cost in Spiking and Non-Spiking Neural Networks — CSEM
Discover how the neuromorphic ecosystem is moving beyond traditional von Neumann computing toward more adaptive, event-driven, energy-efficient AI at the edge.