TPU vs GPU: How Google's Tensor Processors Accelerate AI Workloads

TPU (Tensor Processing Unit) is a specialized tensor processor developed by Google to accelerate artificial intelligence computations. Unlike standard CPUs and graphics processors (GPUs), a TPU was originally designed specifically for neural networks, which require processing massive numerical datasets and performing billions of mathematical operations.

The main feature of a TPU is its architecture optimized for matrix computations. This enables these processors to efficiently train large language models, handle user requests, and perform other machine learning tasks. Let's explore how Google's tensor processors work, how they differ from GPUs, and in which cases their use is truly justified.

What is a TPU and What Is It Used For?

A TPU (Tensor Processing Unit) is a specialized processor from Google designed to accelerate machine learning. Its primary task is to quickly execute mathematical operations on tensors-multidimensional arrays of numbers used by neural networks.

When processing text, images, or audio, a neural network transforms information into numerical representations and repeatedly performs matrix multiplications, additions, and other operations. The larger the model, the more computational resources are required.

A standard central processing unit (CPU) can perform these calculations, but its architecture is intended for a broad range of tasks. A graphics processing unit (GPU) is much more efficient at handling many operations in parallel. The TPU takes specialization even further: most of its hardware resources are optimized specifically for neural network computations.

Google introduced the first generation TPU in 2016. Initially, these accelerators were used for inference-executing already trained models. Later generations gained the ability to support large-scale training of neural networks.

Today, Google's tensor processors are used primarily in data centers and cloud infrastructure. Their main applications include:

• Neural network training: Processing large datasets and tuning model parameters.
• Inference: Generating responses, recognizing images, and executing other operations with trained models.
• Large language model processing: Accelerating computations required for modern AI systems.
• Massive parallel computations: Combining many TPUs for distributed handling of complex tasks.

However, TPUs are not a universal replacement for CPUs or GPUs. Their advantages are most evident when the computational workload matches their specialized architecture.

How a TPU Works: Architecture and Computational Principles

The operating principle of a TPU is based on parallel processing of large arrays of numbers. Instead of executing many individual instructions, the processor uses specialized computational blocks that simultaneously handle thousands of mathematical operations.

The primary advantage of this architecture is high-speed matrix calculations with relatively low energy consumption per operation.

Matrix Computations and Systolic Arrays

At the core of a TPU are Matrix Multiply Units (MXU), which utilize systolic arrays-structures made up of interconnected computational elements.

Here's how they operate:

• Data enters the computational array.
• Each element performs multiplication and accumulates the result.
• Intermediate values are passed to neighboring elements.
• Many operations occur simultaneously, without constant access to main memory.

This approach is especially effective for neural networks, as much of their workload involves matrix multiplication. For instance, when processing text, a language model repeatedly transforms word representations and calculates relationships between them.

In typical computing systems, moving data between memory and the processor can take significant time and consume extra energy. Systolic architecture reduces such transfers, allowing data to be reused within the computational array.

Memory and Computation Optimization

To ensure efficient operation, TPUs need not only high computational power, but also rapid data transfer. Modern accelerators use high-speed HBM memory and internal storage for intermediate results.

The precision of numerical computations is also crucial. Neural networks do not always require standard 32-bit accuracy, so TPUs support specialized numerical formats:

• BF16 (bfloat16): A 16-bit format that reduces data volume and speeds up computations while maintaining a wide representable range.
• INT8: 8-bit integers used in quantized models to decrease memory usage and accelerate certain operations.
• FP32: 32-bit format for tasks requiring higher computational precision.

The supported formats and their handling depend on the TPU generation.

The combination of matrix units, high-speed memory, and reduced-precision calculations allows TPUs to efficiently execute neural network operations. However, actual performance depends on the model's architecture, dataset size, and the quality of software optimization.

TPUs belong to the class of specialized integrated circuits known as ASICs. Learn more about why these processors can outperform general-purpose chips in certain tasks in the article ASIC processors: why they are faster and more efficient than standard chips.

TPU vs. GPU: Differences and Which Is More Efficient

Both TPUs and GPUs can accelerate neural network computations, but they differ in architecture and versatility. GPUs are designed for a wide range of parallel tasks, while TPUs specialize in operations typical for machine learning.

Graphics processors were originally built for image and 3D graphics processing. Later, their ability to handle numerous simultaneous computations made them valuable for artificial intelligence.

TPUs, on the other hand, were developed directly for neural networks. Their architecture is optimized for matrix operations, which means, under the right workload, they can deliver high performance and energy efficiency.

Why TPUs Aren't Always Faster Than GPUs

TPU specialization does not guarantee automatic superiority over NVIDIA or AMD graphics cards. Performance depends on model size, computational characteristics, software stack, and how efficiently hardware blocks are loaded.

Modern GPUs also feature specialized matrix operation accelerators. For example, NVIDIA graphics cards use Tensor Cores, which speed up AI computations.

To learn more about their structure and applications, read the article Tensor Cores in NVIDIA graphics cards: accelerating AI and graphics.

GPUs are often more convenient for experimentation, developing new neural network architectures, and tasks requiring non-standard operations. TPUs are especially attractive for large-scale training and inference of models well-optimized for their architecture.

The choice between TPU and GPU should be based on the performance of your specific model, computational costs, and support for required tools-not just the theoretical power of the processors.

Where Google Tensor Processors Are Used

TPUs are mainly used in Google's data centers and cloud infrastructure. They accelerate neural networks that require enormous computational resources for training and handling requests.

Training Large Neural Networks

Training language models involves repeatedly processing large volumes of data and updating billions of parameters. For such tasks, Google combines many TPUs into compute clusters.

The processors work together to perform matrix operations and exchange intermediate results via high-speed connections. This makes it possible to distribute the computational load and train models that couldn't efficiently fit on a single accelerator.

TPUs are used in Google's infrastructure for developing and training artificial intelligence models, including the Gemini family.

Inference and Request Processing

Once trained, a neural network must process user requests-a process called inference.

For example, when generating text, a language model sequentially calculates the probabilities of the next tokens. TPUs speed up these mathematical operations, allowing a large number of requests to be served.

For inference, not only computational power matters, but also response latency, memory bandwidth, and the cost per request.

Google Cloud TPU

Google provides access to its tensor processors through the Google Cloud platform. Developers can rent computing resources without purchasing their own hardware.

Cloud TPU is used for training and running neural networks with supported machine learning tools, including JAX and PyTorch/XLA.

However, TPUs are not meant to replace standard gaming graphics cards. Their architecture is not designed for graphics APIs, game rendering, or outputting images to a monitor. For such tasks, GPUs remain the much better choice.

Advantages and Limitations of TPUs

Google's tensor processors accelerate artificial intelligence workloads, but their effectiveness depends on the nature of computations. The specialized architecture gives them advantages for some tasks and limitations in others.

Main advantages of TPUs:

• High performance: Matrix units enable simultaneous execution of many operations required for training and running neural networks.
• Energy efficiency: Specialized computation schemes help reduce energy costs compared to less suitable hardware.
• Scalability: Multiple TPUs can be combined into large computing systems for handling large AI models.
• Optimization for neural networks: Support for BF16 and other numerical formats accelerates computations and reduces memory usage.

Main limitations of TPUs:

• Limited versatility: TPUs are not suitable for most tasks unrelated to machine learning.
• Software ecosystem dependence: Effective use requires compatibility with supported tools and compilers.
• Optimization challenges: Non-standard operations and unique neural network architectures can decrease accelerator efficiency.
• Limited availability: Unlike GPUs, TPUs are mainly accessible through cloud infrastructure, not as hardware for regular PCs.

In practice, TPUs are particularly useful for large-scale neural network training and inference with heavy matrix operations. For small projects, experiments, and workloads with diverse computational needs, GPUs often prove to be a more convenient and cost-effective solution.

Conclusion

TPUs are specialized processors from Google created to accelerate artificial intelligence. Thanks to their matrix computation units and optimized architecture, they are highly effective for training large neural networks and handling requests.

The main difference between TPUs and GPUs is specialization. GPUs are more versatile and suitable for graphics, development, and machine learning, while TPUs are primarily focused on neural network computations. However, the efficiency of each accelerator depends on the specific task.

For small AI projects and local work, GPUs are usually the more practical choice. TPUs are worth considering for large-scale neural network training and inference in the cloud, when performance, energy efficiency, and computational costs are critical.