So what is a Tensor processing unit (TPU) and why will it be the future of Machine Learning?

What is a Tensor Processing Unit (TPU)? So, for sure, you will know what a CPU is – the main processor of each device. Intel, AMD, and Qualcomm have produced such chips for your devices for a really long time. Here, you can check out some of the most famous CPUs of all time.  
GPU is probably a term that you know too. A GPU is the Graphical Processing Unit that helps you display 3D video games on your device, and also serves to train AI.
But have you ever heard of a TPU?   

What is a Tensor Processing Unit (TPU)? 

TPU stands for Tensor Processing Unit, and it is a type of application-specific integrated circuit (ASIC), created by Google. ASIC devices became popular during the cryptocurrency boom. These processors are highly specific, made for a very small range of purposes like matrix multiplications, and tensor operations. Contrary to CPUs and GPUs, which are created for general purposes, the main purpose of the TPUs is to accelerate machine learning workloads. It cannot be used for multiple applications or to process a heavy 3D game. The TPUs are optimized for high-throughput, with low-power consumption, and low-latency operations required for training neural networks (Machine Learning).
Basically, TPU serves to train machine learning models, cheaper and more efficient than other solutions and still provides plenty of computing power.
Currently, TPUs are an integral part of Google’s Cloud infrastructure, and they power different AI applications like image recognition, chatbots, natural language processing, synthetic speech, recommendation engines, and more.  
Google uses them in Google Photos, Google Translate, Google Assistant, Google Search, and even Gmail.  

 How do TPUs work? 

TPUs work differently from CPUs and GPUs. They load the parameters from memory and put them into the matrix which they use for multiplication, and after that, they load data from memory. Then, when multiplication is happening, the results are passed from one multiplier to the next, while simultaneously taking summation. No memory access is necessary during the process even though the calculation and data passing could be massive.  

The history of Tensor Processing Unit (TPU) 

Google started using TPUs for internal purposes back in 2015, but it took a long time before they became available to the public. In 2016, at Google I/O, the company’s development conference, Google announced showing the TPUs to the world. The tech giant explained what they are, showed the TensorFlow framework, and talked more about the Tensor Cores. What Google bragged about was that these new processing units would enhance the performance of Machine Learning models. The TPU v1 was mostly designed for inferencing tasks and delivering significant improvement in power efficiency over CPUs and GPUs at the time. TPU v2 and v3 expanded their use by including both training and inference, offering higher performance and greater scalability. TPU v4 and TPU 5 (TPUv5e and TPUv5p) further improved the processors and their capabilities.  

TPU versions: 

• TPU v1 – 23 TOPS (Tera Operations Per Second), 3 GB DDR3 RAM and TDP of 75 W. 

• TPU v2 – 45 TOPS, 16 GB HBM and TDP of 280 W. 

• TPU v3 – 123 TOPS, 32 GB HBM. and TDP of 220 W. 

• TPU v4 – 275 TOPS, 32 GB HBM, and TDP of 170 W. 

• TPU v5e – 197 (bf16) TOPS, 393 (int8) TOPS, and 16 GB HBM. 

• TPU v5p – 459 (bf16) TOPS, 918 (int8) TOPS, and 95 GB HBM. 

• TPU v6 – Yet to be announced, but it is expected to have a 4.7 times performance increase in comparison to TPU v5e, but this cannot be verified at the moment.

What are Tensor Cores? 

The secret to TPU productivity is the Tensor Cores. They are the small units, inside each TPU, that handle the specific mathematical operations, involved in training and running Machine Learning models. In contrast to cores inside CPUs and GPUs, the Tensor Cores perform matrix multiplication additions at high speed, which are fundamental operations in deep learning algorithms. By parallelizing these tasks and optimizing them for neural network workloads, Tensor Cores significantly accelerate the computation process, allowing shorter training times and more efficient inference. 

 What is TensorFlow? 

TensorFlow is a Machine Learning framework created by Google. It is an open-source and it offers a comprehensive ecosystem for building and deploying machine learning models, including tools for model training, data preprocessing, and serving. The idea behind it is to be flexible and scalable, so Google can offer it as a cloud service. TPUs are tightly integrated with TensorFlow, enabling developers to leverage the power of TPUs seamlessly within their TensorFlow workflows.

Advantages of the TPU 

• High performance. For some specific Machine Learning tasks, this type of processor offers superior performance. TPUs can dramatically reduce training times and speed up inference. The Tensors can execute multiple operations in parallel.  

• Energy efficiency. Efficiency is a top concern, and these processors really excel at providing high computational power without consuming excessive energy. 

• Scalability. Google created TPUs with the idea of being easily scalable and to be able to handle large Machine Learning workflows.  

• Integration with TensorFlow. TPUs work seamlessly with TensorFlow, making it easier for developers to deploy machine learning models at scale. 

• Can accelerate different AI workloads like PyTorch, and JAX.   

Disadvantages of TPU 

• Cost. Depending on the size of your project, especially if the project is small, the price can be too expensive in comparison to other solutions.  

• Limited Flexibility. TPUs are far less versatile in comparison to CPUs and GPUs for other types of computations. They are good for neural network workloads only. 

• Dependency on Google Cloud. You can’t simply buy a device with TPUs inside. To use them, you need to pay for a Google Cloud subscription which creates a strong dependency.  

 When to Use TPU Instead of CPU or GPU ?

Use TPUs when you need to: 

• Train large and complex Machine Learning models quickly. 

• Perform inference on large-scale models with high throughput requirements. 

• Optimize energy efficiency for Machine Learning tasks. 

• Use TPUs for Models with a long training period – weeks or months. 

• Train models are dominated by matrix computations. 

• Leverage the tight integration with TensorFlow for seamless deployment. 

When to Use GPU Instead of CPU or TPU ?

Use GPUs when you need to: 

• Train models that are not written in TensorFlow. 

• Must work with models with many custom TensorFlow operations that partially need to run on CPUs. 

• Use models where the problem is that the source does not exist or is too onerous to change. 

• Work with models with TensorFlow ops not accessible on Cloud TPU. 

• Use medium and large models with big batch sizes. 

• Train and run machine learning models that are not exclusively neural networks. 

• Handle a variety of computational tasks beyond just machine learning. 

• Benefit from a larger community and broader software support for machine learning frameworks. 

 When to Use CPU Instead of TPU or GPU?

Use CPUs when you need to: 

• Perform general-purpose computing tasks. 

• Run workloads that do not require the specialized acceleration provided by TPUs or GPUs. 

• Handle tasks that benefit from the versatility and single-thread performance of CPUs. 

• Make quick prototyping. 

• Work with simple models with a short training period. 

• Small models with small batch sizes. 

• Use custom TensorFlow operations that are written in C++. 

 TPU vs GPU vs CPU comparison table  

Parameter	TPU (Tensor Processing Unit)	GPU (Graphics Processing Unit)	CPU (Central Processing Unit)
Primary Use	Machine learning and AI workloads	Graphics rendering, machine learning, and parallel processing	General-purpose computing and serial processing
Architecture	Custom ASIC (Application-Specific Integrated Circuit)	Many-core architecture	Few cores with high clock speed
Performance	Optimized for matrix multiplications and tensor operations	High parallelism for massive computation tasks	High single-thread performance
Energy Efficiency	High for specific ML tasks	Moderate, depends on the workload	Varies, generally lower for intensive computations
Programming Frameworks	TensorFlow, custom frameworks	CUDA, OpenCL, DirectCompute	General-purpose languages (C, C++, Python, etc.)
Latency	Low for ML tasks	Low for parallel tasks, higher for serial tasks	Low for serial tasks, higher for parallel tasks
Cost	High, especially for state-of-the-art versions	High, especially for high-end models	Varies, generally lower than GPUs and TPUs
Memory Bandwidth	High for tensor operations	High for graphics and parallel processing tasks	Moderate to high, depending on the model
Flexibility	Low, designed for specific tasks	Moderate, versatile but best for parallel tasks	High, versatile for a wide range of applications

 

Should you try out a TPU for your Machine Learning needs? 

It depends on your specific needs. If you are thinking about a small Machine Learning project, you are better off with a simple CPU. If you are searching for a solution for large neural network training, check if you need TensorFlow or not. If you don’t, you can go for a GPU solution for training AI.
If your project is large, involves neural network training, or inference and you need high efficiency and high performance, then TPUs can be an excellent choice.
Just be sure to make a good evaluation of your workload and performance goals, before you jump to conclusions and make your choice.