18.3 A 0.5V 54μW ultra-low-power recognition processor with 93.5% accuracy geometric vocabulary tree and 47.5% database compression

Microwatt object recognition is being considered for many applications, such as autonomous micro-air-vehicle (MAV) navigation, a vision-based wake-up or user authentication for the smartphones, and a gesture recognition-based natural UI for wearable devices in the Internet-of-Things (IoT) era. These applications require extremely low power consumption, while maintaining high recognition accuracy - constraints that arise because of the requirement for continuous heavy vision processing under limited battery capacity. Recently, a low-power feature-extraction accelerator operating at near-threshold voltage (NTV) was proposed, however, it did not support the object matching essential for the object recognition [1]. Even state-of-the-art object matching accelerators consume over 10mW, thereby making them unsuitable for an MAV [2, 3]. Therefore, an ultra-low-power high-accuracy recognition processor is necessary, especially for MAVs and IoT devices.