Neuromorphic visual attention for Sign-language recognition on SpiNNaker

ArXi:2605.06005v1 Announce Type: new Sign-language recognition has achieved substantial gains in classification accuracy in recent years; however, the latency and power requirements of most existing methods limit their suitability for real-time deployment. Neuromorphic sensing and processing offer an alternative paradigm based on sparse, event-driven computation that s low-latency and energy-efficient perception. In this work, we