Why FPGA is Better than GPUs for AI and Deep Learning Applications

Growing with the remarkable development of digital data of images, videos, and speech from sources, for example, online media and the internet-of-things is driving the requirement for analytics to make that information justifiable and noteworthy. Data analytics frequently depend on machine learning (ML) algorithms. Among ML algorithms, deep convolutional neural networks (DNNs) offer cutting edge precision for significant image classification errands and are getting widely adopted. The renewed interest in artificial intelligence in the previous decade has been a boon for the graphics cards industry. Organizations like Nvidia and AMD have seen an immense lift to their stock prices as their GPUs have demonstrated to be effective for training and running deep learning models.