Overview

   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 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) consume too much power, resulting in <10 hours operation time with a 190mAh coin battery.

Implementation results

Performance comparison

 

Architecture

Features

  - Analog & Digital Haar-like Filtering

  - Workload Reduction with Separable Filter Convolution

  - Transpose-Read SRAM

Related Papers

  - ISSCC 2017 [pdf]