The 2011 paper “Flexible, High Performance Convolutional Neural Networks for Image Classification” by Dan C. Cireşan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, and Jürgen Schmidhuber was a pioneering work in the field of deep learning. It was the first to parallelize the computations required for training Convolutional Neural Networks (CNNs) using a GPU (Graphical Processing Unit). This approach significantly accelerated the training process, allowing for the efficient handling of large datasets and more complex neural network architectures. By leveraging the parallel processing capabilities of GPUs, the authors demonstrated substantial improvements in training speed and model performance, setting new benchmarks in image classification tasks. This breakthrough laid the groundwork for the widespread adoption of GPU-accelerated deep learning, influencing subsequent research and development in the field.