Title: CNNP-v2: An Energy Efficient Memory-Centric Convolutional Neural Network Processor Architecture
Authors: Sung-Pill Choi, Kyeong-Ryeol Bong, Dong-Hyeon Han, and Hoi-Jun Yoo
An energy efficient memory-centric convolution-al neural network (CNN) processor architecture is proposed for smart devices such as wearable devices or internet of things (IoT) devices. To achieve energy-efficient processing, it has 2 key features: First, 1-D shift convolution PEs with fully distributed memory architecture achieve 3.1TOPS/W energy efficiency. Compared with conventional architecture, even though it has massively parallel 1024 MAC units, it achieve high energy efficiency by scaling down voltage to 0.46V due to its fully local routed design. Next, fully configurable 2-D mesh core-to-core interconnection support various size of input features to maximize utilization. The proposed architecture is evaluated 16mm2 chip which is fabricated with 65nm CMOS process and it performs real-time face recognition with only 9.4mW at 10MHz and 0.48V.
