What is Tensor Processing Unit (TPU)?

Discover what a Tensor Processing Unit (TPU) is, how it accelerates machine learning tasks, and its role in enhancing AI performance with efficient data processing capabilities.

Table of Contents

Context: Google has recently launched Ironwood, its seventh-generation Tensor Processing Unit (TPU).

About Tensor Processing Unit (TPU)

Tensor Processing Unit (TPU) is an application-specific integrated circuit (ASIC) designed exclusively for accelerating machine learning (ML) tasks, like deep learning.
They are built specifically to handle operations involving tensors (multi-dimensional arrays used in ML models).

Key Features of TPUs

Designed for Machine Learning: Optimised for tensor operations, which are the foundation of neural networks.
High Performance: TPUs offer significantly faster computation compared to CPUs and GPUs for ML tasks.
- Training that takes weeks on GPUs can be completed in hours using TPUs.
Parallelism: Like GPUs, TPUs also use parallel processing, but are even more specialized. They can handle millions of tensor operations simultaneously.
Energy Efficiency: More energy-efficient than GPUs and CPUs when running AI workloads.

Key Differences Between CPU, GPU and TPU

Feature	Centra Processing Unit	Graphic Processing Unit	Tensor Processing Unit
Purpose	General Computing	Graphics & parallel computing	AI & ML-specific tasks
Processing Type	Sequential	Parallel	Tensor-based, parallel
Efficiency in AI	Low	High	Very High