Tensor Processor

A Tensor Processor is a type of specialized hardware designed to accelerate machine learning tasks, particularly those involving tensor computations, which are mathematical operations on multi-dimensional data arrays. Tensors are a fundamental data structure in deep learning and are used to represent input data, weights, and outputs in neural networks.

Tensor Processors are optimized for the high parallelism and computational requirements of deep learning algorithms, allowing them to perform large matrix multiplications and other tensor operations more efficiently than general-purpose CPUs or GPUs. They are often characterized by a high number of cores tailored for specific operations such as matrix multiplication, which is a key component in training and inference stages of neural networks.

These processors can be found in various forms, including dedicated chips like Google’s Tensor Processing Units (TPUs) and other proprietary designs from companies focusing on artificial intelligence and machine learning. The goal of Tensor Processors is to significantly reduce the time and power required to train and run deep learning models, making them essential tools in the development of AI applications.