GPUs are pivotal in the field of AI, especially for training deep neural networks, due to their highly parallel structure which makes them more efficient than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel. This efficiency is leveraged in AI to speed up the training of models on large datasets and to facilitate more complex, computationally intensive neural network architectures. The ability of GPUs to handle multiple computations simultaneously makes them ideal for the matrix and vector operations that are common in machine learning and deep learning tasks. This capability has significantly reduced the time required for training models, enabling the practical application of AI technologies across various domains including vision, language processing, and autonomous vehicles.
Historical overview: The use of GPUs in AI surged in the late 2000s as researchers and practitioners began to exploit their parallel processing capabilities for deep learning tasks. Nvidia, a leading GPU manufacturer, played a crucial role in this shift with the introduction of CUDA (Compute Unified Device Architecture) in 2007, a parallel computing platform and application programming interface (API) model that allows software developers to use GPUs for general purpose processing.
Key contributors: Nvidia is a key player in the development and popularization of GPUs for AI, with its CUDA platform enabling widespread adoption among researchers and developers. Other companies like AMD and Intel have also contributed to the advancement of GPU technologies for computing and AI applications.