14.2 A 65nm 24.7µJ/Frame 12.3mW Activation-Similarity-Aware Convolutional Neural Network Video Processor Using Hybrid Precision, Inter-Frame Data Reuse and Mixed-Bit-Width Difference-Frame Data Codec
Zhe Yuan, Yixiong Yang, Jinshan Yue, Ruoyang Liu, Xiaoyu Feng, Zhiting Lin, Xiulong Wu, Xueqing Li, Huazhong Yang, Yongpan Liu
Abstract
Convolutional Neural Networks (CNNs) have become widely used in image signal processing, such as tracking, classification and post-processing. Modern CNNs use millions of weights and activations, leading to critical challenges for both computation and data transmission. Video applications, such as autopilot and surveillance cameras, have to process a large number of sequential images/frames within limited time, making the situation even worse. As shown in Fig. 14.2.1, adjacent activation frames of typical video applications are similar to each other most of the time, providing an opportunity to reduce both computing and data transmission complexity significantly.