Tensor cores are specialized processing units designed to accelerate machine learning tasks, particularly those involving deep learning. They are optimized for performing matrix multiplications and other tensor operations, which are fundamental in training and running neural networks. By handling these calculations more efficiently than traditional cores, tensor cores can significantly speed up the processing time for complex models. These cores are commonly found in NVIDIA graphics processing units (GPUs) and are used in various applications, including image recognition, natural language processing, and scientific simulations. Their ability to perform mixed-precision calculations allows for faster computations while maintaining accuracy, making them essential for modern AI workloads.