A FPGA-based neural accelerator for small IoT devices

Abstract

Neural network has been widely used for various applications. While most of previous approaches tried to use large neural networks such as convolutional neural network (CNN) and deep neural network (DNN), these heavy models are hardly adapted to IoT(internet of things) platforms due to their limited resources. This work proposes a compact neural network accelerator for IoT devices. Our design shows 11.95 GOP/s total throughput and 413.99mW power consumption with 98.04% accuracy.