FPGAs or GPUs, that is the question. Since the popularity of using machine learning algorithms to extract and process the information from raw data, it has been a race between FPGA and GPU vendors to offer a HW platform that runs computationally intensive machine learning algorithms fast and efficiently. As Deep Learning has driven most of the advanced machine learning applications, it is regarded as the main comparison point. Even though GPU vendors have aggressively positioned their hardware as the most efficient platform for this new era, FPGAs have shown a great improvement in both power consumption and performance in Deep Neural Networks (DNNs) applications, which offer high accuracies for important image classification tasks and are therefore becoming widely adopted [1]. As there are various tradeoffs to consider, it is hard to answer with just a "Yes" or "No".