Detailed Explanation: In the context of AI and machine learning, being "GPU-poor" means having insufficient access to high-performance graphics processing units (GPUs), which are critical for training and running complex models. GPUs are designed to handle parallel processing tasks much more efficiently than traditional CPUs, making them essential for deep learning and other data-intensive applications. Limited access to GPUs can significantly slow down the development and deployment of AI models, as it restricts the ability to handle large datasets and complex computations quickly.
Historical Overview: The term "GPU-poor" likely emerged alongside the increasing demand for GPUs in the AI and machine learning fields, particularly from the mid-2010s onwards. As AI models grew in complexity and size, the competition for GPU resources intensified, especially with the advent of large-scale neural networks and deep learning techniques around 2012-2015.
Key Contributors: The significance of GPUs in AI was highlighted by researchers like Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, whose work in deep learning demonstrated the necessity of GPU acceleration. Companies like NVIDIA, which developed CUDA, a parallel computing platform and application programming interface (API) model, have been instrumental in advancing GPU technology for AI applications.