Context:

Google recently launched a new computer chip called Ironwood. This chip is the company’s seventh-generation TPU, or Tensor Processing Unit. It has been designed to run artificial intelligence (AI) models faster and more efficiently.

Processing Units: The Computational Core

Processing units are essential hardware components that perform tasks ranging from basic arithmetic to complex data processing. Acting as the “brain” of a computer, they are comparable to how the human brain carries out different mental functions.

·       Central Processing Unit (CPU): Developed in the 1950s, the CPU is a general-purpose processor that manages and coordinates various hardware components. It operates sequentially and executes a wide range of tasks.

o   Modern CPUs have multiple cores—typically two to sixteen—each capable of executing instructions. More cores improve multitasking, though CPUs with two to eight cores suffice for most everyday tasks.

·       Graphics Processing Unit (GPU): Unlike CPUs, GPUs are designed for parallel processing. Originally built for graphics rendering in video games and animations, GPUs now handle broader functions, especially in machine learning. They contain thousands of cores, allowing them to break down and process complex problems simultaneously. This makes them more efficient than CPUs for large datasets and repetitive tasks.

o   However, GPUs haven’t replaced CPUs. Instead, they serve as co-processors, assisting CPUs in data-intensive applications where parallel computing offers an advantage.

·       Tensor Processing Unit (TPU): Introduced by Google in 2015, TPUs are application-specific integrated circuits (ASICs), purpose-built for AI and machine learning. They are optimized for tensor operations, which are key to neural networks. TPUs process large data volumes rapidly, significantly reducing AI model training time—from weeks with GPUs to just hours with TPUs.

About Ironwood

Ironwood is Google’s seventh-generation Tensor Processing Unit (TPU), launched at Google Cloud Next ’25. It is the company’s most powerful, scalable, and energy-efficient AI accelerator to date, and the first TPU specifically designed for AI inference—enabling proactive data interpretation rather than just responsive outputs.

Key highlights:

• Purpose-built for inferential AI in the "age of inference," where AI agents generate insights proactively.
• Scales up to 9,216 liquid-cooled chips with advanced Inter-Chip Interconnect (ICI) networking.
• Part of Google Cloud’s AI Hypercomputer architecture, which integrates hardware and software for optimal AI performance.
• Compatible with Google’s Pathways software stack, enabling developers to harness vast computing power easily.

Conclusion

From general-purpose CPUs to highly specialized TPUs, the evolution of processing units reflects the growing demand for faster, more efficient computing. Google’s Ironwood TPU represents a major advancement in this field, especially in AI and machine learning. As businesses and researchers continue to tackle increasingly complex AI challenges, processors like Ironwood will play a crucial role in shaping the future of intelligent computing.