What is a Tensor Processing Unit (TPU)?
Tensor Processing Units (TPUs) are custom-built hardware chips developed by Google to speed up AI workloads like machine learning and neural networks. They’re optimised for processing tensors, which makes them the perfect fit for deep learning models.
What is a Tensor Processing Unit?
A Tensor Processing Unit is a processor designed specifically for machine learning. Unlike general-purpose CPUs or GPUs, TPUs are built to execute the matrix and vector operations that power neural networks at high speed. Google launched the first TPU in 2016 and, since then, several generations have followed. TPUs are efficient at processing tensors, making them a powerful tool for large-scale AI workloads.
TPUs are built into Google Cloud and are designed to work with frameworks like TensorFlow. Their architecture is optimised for low latency and high throughput, which significantly shortens both training and AI inference times. TPUs include purpose-built matrix units capable of performing thousands of operations in parallel. They also use less energy than traditional processors, making them ideal for both research and live deployment.
How do TPUs work?
TPUs are specifically designed for efficient tensor processing. How they work can be summarised as follows:
- Tensors as input: Tensors are multidimensional, array-like data structures that form the backbone of most neural networks.
- Matrix Multiply Units (MXUs): These units handle large-scale matrix operations fast.
- Systolic arrays: Data flows through these arrays in a steady rhythm, which makes them ideal for parallel processing.
- On-chip memory: Large, directly attached memory reduces delays from data transfers and speeds up computations.
- Training and inference: TPUs support both training and inference, with some generations optimised more for one than the other.
- Software integration: Frameworks like TensorFlow (and others) work with TPUs through optimised compiler steps that translate tensor operations into efficient TPU code. This ensures the TPU is used efficiently.
Modern TPU generations like Trillium and Ironwood include additional hardware features, such as SparseCores, that boost performance on specialised AI workloads like embeddings. The XLA compiler (Accelerated Linear Algebra) also plays a key role in efficiency. It translates tensor operations from frameworks like TensorFlow into code optimised specifically for TPUs.
How do CPUs, GPUs and TPUs differ?
CPUs (Central Processing Units) are general-purpose processors that can handle a wide range of tasks, but they’re not built for large-scale parallel processing. GPUs (Graphics Processing Units) are designed for processing large volumes of data in parallel, especially for rendering graphics and performing numerical computations. TPUs, by contrast, are built for machine learning and optimised for the matrix operations that are central to neural networks. While GPUs use thousands of general-purpose cores for parallel computing, TPUs rely on dedicated matrix units that process large tensor computations faster and more efficiently. Because TPUs are purpose-built for this type of processing, they’re also more energy-efficient for AI tasks. CPUs are still essential for general control, but TPUs are better suited for the compute-heavy operations that drive AI models. In cloud environments, they also make it easier to run and scale complex models that would be hard to manage on conventional GPUs.
| Feature | CPU | GPU | TPU |
|---|---|---|---|
| Best suited for | General-purpose tasks | Processing data in parallel | Tensor operations (AI) |
| Compute units | Few high-performance cores | Many general-purpose cores | Dedicated matrix units |
| Energy efficiency | Medium | Medium | High for AI tasks |
| Common use cases | Operating systems, apps | Graphics rendering, some AI tasks | AI training and inference |
| Memory access | General-purpose | Highly parallel | Direct, on-chip memory optimised for AI workloads |
TPUs are mostly found in Google Cloud, while GPUs are used across a wide range of contexts.
Where are TPUs used?
TPUs are used wherever large amounts of data and complex models need to be processed. They are widely used in AI, cloud computing and data analytics because they significantly reduce the time it takes to train neural networks.
Artificial intelligence
TPUs are primarily used for machine learning and deep learning because they are capable of speeding up compute-intensive workloads. They allow complex models to be trained in far less time than traditional CPUs or GPUs. Common use cases include AI image recognition, automatic speech recognition and natural language processing.
Their high level of parallelism allows TPUs to handle models with billions of parameters at scale. This makes them a great fit for large transformer architectures. They also support faster iteration and model tuning, which is critical in both research and commercial AI development.
Cloud computing
By integrating TPUs directly into its cloud platform, Google gives businesses and developers access to powerful AI computing resources without needing to invest in their own hardware. Cloud computing allows model training workloads to easily scale up or down from small experiments to large-scale training projects. TPUs also speed up both training and inference, helping bring models into production more quickly. As a result, organisations can use AI at scale without expanding or maintaining local infrastructure.
Edge computing
Google also offers specialised Edge TPUs designed to run smaller models on end devices. Using this kind of TPU within an edge computing setup allows data to be processed in real-time and without needing to be sent to distant data centres. Edge TPUs are often used in autonomous vehicles, smart cities and industrial IoT systems. Running inference on the device reduces latency, saves bandwidth and offers data privacy advantages by keeping information local.
Data analytics
TPUs are also increasingly being used to process large and complex datasets. In AI-powered data analysis, they allow complex analyses and predictive models trained on extensive datasets to be run faster. This helps businesses and research institutions handle financial data, medical records or real-time streaming data more quickly and at larger volumes.
Research and development
TPUs are also used in scientific research to train AI models for simulations, data analysis and experimental work. Their ability to handle large datasets and perform tensor operations at high speed helps reduce the time needed for experiments and simulations. This, in turn, accelerates hypothesis testing, model tuning and result validation. As a result, TPUs are ideal for handling complex or data-heavy projects, where they support faster, more efficient development cycles.