A Lightweight Spatiotemporal Network For Online Eye Tracking

In our latest conference paper, an exploration of BrainChip neuromorphic techniques in event-based data processing, tailored specifically for optimizing efficiency in edge computing environments. Discover the foundational advancements driving efficiency and efficacy in event-based data processing, providing invaluable insights for researchers and practitioners in the field of edge computing.

Here’s what you’ll uncover:

– Causal Spatiotemporal Convolutional Network: A detailed examination of our thoroughly designed network, engineered to seamlessly handle event data while maximizing performance on hardware with limited resources.

– Efficiency Optimization Strategies: In-depth insights into our approach, including the deliberate simplification of architecture, strategic buffering for online inference, and achieving remarkable activation sparsity exceeding 90%.

– Affine Augmentation Strategy: An exploration of our innovative methods aimed at mitigating dataset scarcity challenges inherent in event-based systems.

– Real-world Performance Evaluation: A comprehensive analysis of our solution’s performance, demonstrated through its outstanding achievement in the AIS 2024 event-based eye tracking challenge, with a notable score of 0.9916 p10 accuracy on the Kaggle private test set.”

Brainchip proving out AKIDA’s capacity for energy efficient eye tracking having application across industry including automotive, aeronautics, computing, production, safety, security, retail, health etc

My opinion only DYOR

Fact Finder