Smart glasses are rapidly gaining advanced functionality thanks to
cutting-edge computing technologies, accelerated hardware architectures, and
tiny AI algorithms. Integrating AI into smart glasses featuring a small form
factor and limited battery capacity is still challenging when targeting
full-day usage for a satisfactory user experience. This paper illustrates the
design and implementation of tiny machine-learning algorithms exploiting novel
low-power processors to enable prolonged continuous operation in smart glasses.
We explore the energy- and latency-efficient of smart glasses in the case of
real-time object detection. To this goal, we designed a smart glasses prototype
as a research platform featuring two microcontrollers, including a novel
milliwatt-power RISC-V parallel processor with a hardware accelerator for
visual AI, and a Bluetooth low-power module for communication. The smart
glasses integrate power cycling mechanisms, including image and audio sensing
interfaces. Furthermore, we developed a family of novel tiny deep-learning
models based on YOLO with sub-million parameters customized for
microcontroller-based inference dubbed TinyissimoYOLO v1.3, v5, and v8, aiming
at benchmarking object detection with smart glasses for energy and latency.
Evaluations on the prototype of the smart glasses demonstrate TinyissimoYOLO's
17ms inference latency and 1.59mJ energy consumption per inference while
ensuring acceptable detection accuracy. Further evaluation reveals an
end-to-end latency from image capturing to the algorithm's prediction of 56ms
or equivalently 18 fps, with a total power consumption of 62.9mW, equivalent to
a 9.3 hours of continuous run time on a 154mAh battery. These results
outperform MCUNet (TinyNAS+TinyEngine), which runs a simpler task (image
classification) at just 7.3 fps per second.

本文说明了在智能眼镜中设计和实现利用新型低功耗处理器的小型机器学习算法，以实现持续不间断运行。我们使用两个微控制器和蓝牙低功耗模块为研究平台设计了智能眼镜原型，集成了用于能耗和延迟评估的基于 YOLO 的小型深度学习模型 TinyissimoYOLO，并在原型上进行了评估，结果表明其具有良好的检测精度和能耗性能。