Smart eyewear promises unobtrusive, context-aware human–computer interaction by leveraging strategically placed multimodal sensors and on-device intelligence. However, integrating high-bandwidth sensing and machine learning inference within a compact and lightweight form factor remains challenging due to strict constraints in power consumption, memory footprint, and computational efficiency.This work presents RVEdge-Vision, an open hardware and software platform built on the RISC-V ecosystem that enables rapid prototyping and evaluation of next-generation smart glasses. The platform adopts a modular architecture supporting both frame-based and event-based vision sensors. To the best of our knowledge, this is the first open smart-glasses platform integrating event-based vision sensing in a glasses form factor, enabling ultra-efficient visual perception for wearable edge AI systems.
The system incorporates a hardware–software co-designed power management framework optimized for battery-operated edge devices and continuous sensing workloads. As a reference implementation, we present a compact smart-glasses prototype that integrates multimodal sensing and on-device ML acceleration. The device can operate for several hours on a 300 mAh battery while sustaining real-time embedded vision workloads. A YOLOv8-based hand gesture recognition runs on-board with a few ms latency without relying on cloud connectivity. By releasing the platform as open hardware, OpenEdge RV aims to accelerate innovation within the RISC-V and open-edge AI communities, providing a reproducible foundation for research in wearable sensing, neuromorphic vision, and ultra-efficient on-device intelligence.