tensorkiihdytys

Tensorkiihdytys is a Finnish term that translates to "tensor acceleration" in English. It refers to the use of specialized hardware, primarily tensor processing units (TPUs) or tensor cores found in some GPUs, to speed up computations involving tensors. Tensors are multi-dimensional arrays that are fundamental to many modern machine learning and deep learning algorithms.

These specialized processors are designed to efficiently handle the matrix multiplications and other linear algebra operations that are prevalent in neural network training and inference. By optimizing these operations, tensorkiihdytys allows for significantly faster processing of large datasets and more complex models. This acceleration is crucial for making computationally intensive tasks, such as image recognition, natural language processing, and scientific simulations, practical and timely.

The development of tensorkiihdytys hardware has been a key driver in the recent advancements in artificial intelligence. It enables researchers and developers to experiment with larger and more sophisticated models, leading to improved accuracy and capabilities. While TPUs are specifically designed for tensor operations, the integration of tensor cores into general-purpose GPUs has also broadened access to this type of acceleration for a wider range of applications and users. The concept is central to deploying AI at scale and in real-time applications.