A Complete Overview of Tensor Processing Units (TPUs)
Executive Summary
A Tensor Processing Unit, or TPU, is an application-specific integrated circuit (ASIC) developed by Google specifically to accelerate AI and machine learning workloads. These custom processors are engineered to outperform traditional CPUs and GPUs for neural network tasks by efficiently handling a high volume of low-precision computations. A key advantage of TPUs is their superior energy efficiency, enabling more input/output operations per joule, which is critical for large-scale AI operations.
The technology has evolved through several generations, each bringing significant performance gains. The first generation focused on inference using an 8-bit matrix multiplication engine. The second generation introduced floating-point calculations, making TPUs viable for both training and inference. Subsequent generations, including TPU v3 and v4, continued to double performance and improve system-level architecture with larger, more interconnected pods. The most recent versions, like TPU v5e and the latest Trillium (TPU v6), are optimized for performance per dollar and sustainability, cementing their role as a foundational hardware for advanced AI.
The core architecture of a TPU consists of a Matrix Multiplier Unit (MXU) for matrix operations, a large Unified Buffer of on-chip memory, and a dedicated Activation Unit. This specialized design is integral to numerous Google services, including Search, Street View, Photos, and its most advanced AI models like RankBrain and Gemini. Ultimately, TPUs are the essential hardware backbone for Google's large-scale AI model training and inference, powering the next generation of artificial intelligence.
Keywords: Tensor Processing Units, TPUs, AI accelerator, ASIC, Application-Specific Integrated Circuit, Google, neural network, machine learning, deep learning, hardware acceleration, inference, training, CPU, GPU, low-precision computation, energy efficiency, Matrix Multiplier Unit, MXU, Unified Buffer, TPU generations, Trillium, large-scale AI, RankBrain, Gemini
Abbreviations
│
├── AI: Artificial Intelligence
├── ASIC: Application-Specific Integrated Circuit
├── AU: Activation Unit
├── CPU: Central Processing Unit
├── GPU: Graphics Processing Unit
├── ML: Machine Learning
├── MXU: Matrix Multiplier Unit
├── TPU: Tensor Processing Unit
└── UB: Unified Buffer
A Complete Overview of Tensor Processing Units (TPUs)
│
├── What is a TPU?
│ ├── An AI accelerator ASIC (Application-Specific Integrated Circuit)
│ ├── Developed by Google
│ └── Designed for neural network machine learning
│
├── Why TPUs?
│ ├── Outperform CPUs and GPUs for AI tasks
│ ├── High volume of low precision computation
│ └── More input/output operations per joule
│
├── Generations
│ ├── First Generation (TPU v1): 8-bit matrix multiplication engine for inference.
│ ├── Second Generation (TPU v2): Introduced floating-point calculations, making it useful for both training and inference.
│ ├── Third Generation (TPU v3): Twice as powerful as v2, deployed in pods with four times as many chips.
│ ├── Fourth Generation (TPU v4): More than 2x performance over v3, with an "inference" version (v4i) that doesn't require liquid cooling.
│ └── Fifth Generation (TPU v5e & Trillium): TPU v5e offers better performance per dollar for inference. Trillium (TPU v6) is the latest and most powerful/sustainable.
│
├── Architecture
│ ├── Matrix Multiplier Unit (MXU): For matrix operations.
│ ├── Unified Buffer (UB): SRAM that works as registers.
│ └── Activation Unit (AU): Hardwired activation functions.
│
└── Use Cases
├── Google Search
├── Google Street View
├── Google Photos
├── RankBrain
├── Gemini
└── Large-scale AI model training and inference