14.6 A 0.62mW ultra-low-power convolutional-neural-network face-recognition processor and a CIS integrated with always-on haar-like face detector

Recently, face recognition (FR) based on always-on CIS has been investigated for the next-generation UI/UX of wearable devices. A FR system, shown in Fig. 14.6.1, was developed as a life-cycle analyzer or a personal black box, constantly recording the people we meet, along with time and place information. In addition, FR with always-on capability can be used for user authentication for secure access to his or her smart phone and other personal systems. Since wearable devices have a limited battery capacity for a small form factor, extremely low power consumption is required, while maintaining high recognition accuracy. Previously, a 23mW FR accelerator [1] was proposed, but its accuracy was low due to its hand-crafted feature-based algorithm. Deep learning using a convolutional neural network (CNN) is essential to achieve high accuracy and to enhance device intelligence. However, previous CNN processors (CNNP) [2-3] consume too much power, resulting in <;10 hours operation time with a 190mAh coin battery.