Ironwood: 7th Generation TPU | 18 Apr 2025

Source: IE 

Why in News? 

Google has launched a new computer chip, called Ironwood. It is a 7th generation Tensor Processing Unit (TPU) designed to run Artificial Intelligence (AI) models.  

What are the Key Features of Google Ironwood TPU?  

  • AI-Specific Design:  
    • Ironwood is optimized for "thinking models" like Large Language Models (LLMs) and Mixture of Experts (MoEs), enabling proactive AI that generates insights, not just data.  
    • Powerful Performance: Ironwood supports up to 9,216 chips per pod, delivering 42.5 Exaflops of compute, more than 24x the power of the world's largest supercomputer, El Capitan.  
    • Energy Efficiency: Ironwood delivers double the performance per watt compared to the previous generation, using advanced liquid cooling for power efficiency.  
    • Scalable AI Workloads: Ironwood is part of Google Cloud's Hypercomputer architecture, enabling the scaling of generative AI models and supporting the demands of advanced AI tasks. 

What are Processing Units? 

  • About:  Processing units are hardware components that act as the brain of a computer. 
    • They carry out tasks similar to those of the human brain, such as reading, solving math problems, performing calculations, capturing images, or sending messages. 
  • Types of Processing Units:  
    • CPU (Central Processing Unit) 
    • GPU (Graphics Processing Unit) 
    • TPU (Tensor Processing Unit) 

What is a Tensor Processing Unit (TPU)? 

  • A Tensor Processing Unit (TPU) is a type of Application Specific Integrated Circuit (ASIC), Its purpose is to handle a narrow set of specific tasks. 
  • TPUs were specifically developed to accelerate machine learning workloads and to handle AI-specific computational tasks, making them more highly specialized than both CPUs and GPUs. 
  • They are highly efficient at handling large datasets and running complex neural networks, allowing for faster training of AI models compared to traditional processors.      

What is the Difference between CPU, GPU and TPU? 

Feature 

CPU 

GPU 

TPU 

Primary Function 

General-purpose computing 

Parallel processing, graphics rendering 

Accelerating machine learning workloads 

Processing Type 

Sequential processing (can also include some parallelism in modern CPUs) 

Parallel processing 

Parallel processing (optimized for AI tasks) 

Number of Cores 

1 to 16 cores (can be more in advanced CPUs) 

Thousands of cores 

Specialized cores optimized for tensor operations 

Performance for AI 

Not optimized for AI 

Good for AI tasks, especially for large datasets 

Highly optimized for deep learning and neural networks 

Efficiency 

Versatile but less efficient for parallel tasks 

Highly efficient for parallel tasks (e.g., deep learning training, large-scale data processing) 

Extremely efficient for AI, especially neural network training 

Real-World Applications 

    

Personal computing, business applications, software development 

Autonomous vehicles, facial recognition,  video processing, AI model training 

Healthcare AI (e.g., diagnostics), autonomous systems, speech recognition, image recognition 

 

UPSC Civil Services Examination Previous Year Question (PYQ) 

Prelims

Q. With the present state of development, Artificial Intelligence can effectively do which of the following? (2020)

  1. Bring down electricity consumption in industrial units 
  2. Create meaningful short stories and songs 
  3. Disease diagnosis 
  4. Text-to-Speech Conversion 
  5. Wireless transmission of electrical energy 

Select the correct answer using the code given below: 

(a) 1, 2, 3 and 5 only 
(b) 1, 3 and 4 only 
(c) 2, 4 and 5 only 
(d) 1, 2, 3, 4 and 5 

Ans: (b)