Performing underwater exploration with Autonomous Underwater Vehicles (AUV) requires low power and high resolution techniques. New computer vision techniques can be used for underwater image classification on embedded devices. These techniques must face machine resource constraints to offer high performance and low power consumption. This paper presents how to implement a Deep Neural Network (DNN) on Field Programmable Gate Array (FPGA) to perform underwater exploration with an AUV. We introduce tools and methodology to adapt the technology to the underwater context. This paper is part of a work to create an embedded system that can fit into an AUV to perform real time analysis of the underwater environment (using video camera as main sensor) with high autonomy and endurance. This will be achieved by overcoming underwater exploration challenges as : low power consumption, high classification performance, shortage of high-quality labeled data to train algorithm.