Tensor Processing Units (TPUs)

Tensor Processing Units (TPUs) are application-specific integrated circuits (ASICs) developed by Google specifically to accelerate machine learning (AI) workloads. Introduced in 2016, these chips are designed to optimize the training and inference phases of neural networks, particularly for large-scale, complex tasks involving deep learning, natural language processing (NLP), and recommendation systems. 

Key Features and Capabilities

• Specialized Architecture: Unlike general-purpose CPUs or graphics-focused GPUs, TPUs are built with matrix multiplication units (MXUs) that excel at the massive, simultaneous mathematical calculations (tensor operations) required for neural networks.
• High Performance & Efficiency: TPUs can deliver speeds significantly higher than CPUs or GPUs, often offering 15x to 30x better performance for specific, large-batch AI tasks, while consuming less power.
• TensorFlow & Framework Optimization: While tailored for Google's TensorFlow framework, TPUs also support other frameworks, including JAX and PyTorch.
• Scalability (TPU Pods): TPUs can be connected together in large clusters called "Pods," which function as supercomputers for training extremely large AI models (e.g., LLMs) in hours rather than weeks. 

Evolution of TPUs

Since 2015, Google has released seven generations of TPUs, moving from a focus on inference to massive-scale training and, more recently, advanced generative AI: 

• TPU v1 (2015): Focused on inference (running models) for Google services like Search and Photos.
• TPU v2/v3 (2017-2018): Added training capabilities and high-speed inter-chip links.
• TPU v4 (2021): Introduced SparseCores for recommendation models and optical switches.
• Ironwood (v7, 2025): Optimized for generative AI and LLM inference, delivering 4,614 teraflops per chip. 

TPUs vs. GPUs vs. CPUs

• CPU: General-purpose, flexible, but slower for AI-specific matrix math.
• GPU: Highly parallel, versatile, and excellent for varied AI training and graphics.
• TPU: Purpose-built ASIC for matrix operations, offering maximum speed and energy efficiency for large-scale, structured deep learning tasks. 

Usage and Availability

TPUs are not sold as individual physical hardware. They are available primarily through Google Cloud (Cloud TPU), which allows developers to rent TPU-powered infrastructure for training and deploying models, or through Edge TPU for on-device AI applications (e.g., Coral).