Boosting Convolutional Neural Networks Performance Based on FPGA Accelerator

Abstract

Convolutional Neural Network (CNN) has been extensively used for image recognition due to its great accuracy. This accuracy is achieved through emulating the optic nerves behavior in living human beings. The speedy progress of the current applications derived from deep learning algorithms has extra enhanced research and developments. More specifically, several deep CNN accelerators have been planned on FPGA-based platform, due to its fast development round, reconfigure-ability, and high performance. The FPGA is extremely faster than the CPU because it based on parallel mechanism, as well as, consumes very low energy. This paper employs the FPGA in establishing the CNN architecture of type VGG16 model. The FPGA solves the convolutional computations for accelerating the computation time by 11%, without losing much fidelity when using 16-bit fixed-point data format rather than 32-bit floating-point data format.