F-C3D: FPGA-based 3-Dimensional Convolutional Neural Network

In recent years, 3-dimension convolutional neural networks (3D CNNs) have been widely used for video analysis, 3-dimension geometric data and medical image diagnosis. While conventional CNNs are computationally intensive, 3D CNNs push the computational requirements into another level, since each computation depends on multiple image frames. This paper describes a novel hardware architecture for a 3D convolutional neural network, and design strategies to resolve memory usage and bandwidth limitations. The proposed architecture F-C3D is implemented on zc706 at 172MHz, showing 231 times speed up compared with software implementation on 1 GHz ARM CPU, 7.4 times speed up on 3.07 GHz Intel CPU and nearly 10 times lower power consumption than GPU.