4.6 A1.93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications

Recently, deep learning (DL) has become a popular approach for big-data analysis in image retrieval with high accuracy [1]. As Fig. 4.6.1 shows, various applications, such as text, 2D image and motion recognition use DL due to its best-in-class recognition accuracy. There are 2 types of DL: supervised DL with labeled data and unsupervised DL with unlabeled data. With unsupervised DL, most of learning time is spent in massively iterative weight updates for a restricted Boltzmann machine [2]. For a -100MB training dataset, >100 TOP computational capability and ~40GB/s IO and SRAM data bandwidth is required. So, a 3.4GHz CPU needs >10 hours learning time with a -100K input-vector dataset and takes ~1 second for recognition, which is far from real-time processing. Thus, DL is typically done using cloud servers or high-performance GPU environments with learning-on-server capability. However, the wide use of smart portable devices, such as smartphones and tablets, results in many applications which need big-data processing with machine learning, such as tagging private photos in personal devices. A high-performance and energy-efficient DL/DI (deep inference) processor is required to realize user-centric pattern recognition in portable devices.