How TPUs work
AI platforms and their underlying ML models require intensive mathematical processing.
Fundamentally, TPUs are optimized for a type of mathematical operation known as tensor computation.
Tensors are multi-dimensional arrays, or matrixes, that store and process data. Think of them as fundamental data structures or “gears” in machine learning, deep learning, and scientific computing that drive neural network computations and data analysis.
TPUs employ giant groups of multiply-and-accumulate arithmetic logic units (ALUs) that form specialized processing blocks known as tensor cores or matrix multiply units (MXUs). This infrastructure is able to perform addition, multiplication, linear algebra, and convolution, a critical computation in ML that allows systems to extract features from data.
In simple terms, TPUs take in data, break it down into multiple tasks (vectors), simultaneously perform required math on each vector, then return outputs to models.
Tensor operations are foundational to deep learning algorithms because they can process vast datasets simultaneously — including those that incorporate complex data such as images, audio, and video — via parallelism, rapid matrix math, and high memory bandwidth.
