GPU vs. TPU

Key takeaways:

• TPUs are designed specifically for deep learning tasks, offering faster performance for neural network training.

• GPUs are versatile and can be used for a wide range of tasks, including gaming, graphics rendering, and machine learning.

• TPUs are more power efficient than GPUs, making them ideal for large-scale machine learning models.

• GPUs are more widely available and less expensive compared to TPUs.

• TPUs are only accessible through Google Cloud, limiting their availability compared to GPUs.

• GPUs offer better software support for general-purpose computing, while TPUs are optimized for tensor operations and machine learning.

Tensor Processing Units and Graphics Processing Units are specialized hardware accelerators used in machine learning and AI tasks. TPUs are made specifically to speed up neural network computations, but GPUs are adaptable and can be used for various computing applications. Let’s discuss the differences between TPUs and GPUs in detail.

What are TPUs?

TPUs are application-specific integrated circuits (ASICs) exclusively used to train machine learning models. Google developed them to handle the computational demand of the machine learning model. A TPU is a matrix or domain-specific processor, unlike a GPU, which is a general-purpose processor.

Working of TPU

The first step is to receive input data, which is preprocessed as needed for the machine learning model. Then, the model is loaded into memory, and the processed data is propagated through the model forward.
The next step is to add non-linearity using activation functions and operations like max-pooling to manipulate feature maps. Finally, backward propagation computes gradients, and inference compares outputs for predictions.

TPU architecture consists of the following things:

• TPU chip: It has one or more TensorCores. The amount of TensorCores varies depending on the version of the TPU chip. Each TensorCore comprises one or more matrix multiplication units (MXUs), a vector, and a scalar unit.

• Matrix multiply unit (MXU): It is the core computational engine. It is optimized for performing matrix multiplication operations.

• Scalar units: It is used for scalar operations, such as addition, subtraction, multiplication, and activation functions.

• Vector unit: It is used for general computation. It controls flow and calculates memory addresses and other maintenance operations.
  

What are GPUs?

GPUs are specialized electronic circuits designed to render graphics on the screen. They consist of numerous tiny cores used to perform tasks in parallel. GPUs are commonly used in video games, live streaming, data science, and various applications requiring complex mathematical computation.

Working of the GPU

GPUs consist of thousands of small computing units called cores. Because of the parallel utilization of these cores, they can execute several tasks simultaneously. When a computer program or game assigns tasks to the GPU, they are broken down into smaller subtasks. The GPU sends these subtasks to its cores, ensuring the burden is distributed efficiently.

• Control: It supervises the execution of tasks across cores.

• L1 Cache: It keeps frequently accessed data close to the cores for faster retrieval.

• L2 Cache: It provides additional storage capacity and reduces memory latency.

• DRAM: Dynamic RAM is the main memory for storing data.

Pros comparison

Some of the advantages of both processing units are:

Cons comparison

Some of the disadvantages of both processing units are:

GPU vs. TPU

The table below compares the differences between TPU and GPU:

In short, the use of GPUs and TPUs depends on the user's specific needs and budget. GPUs are versatile and can handle multiple tasks simultaneously, whereas TPUs are designed for training deep learning models and are power-efficient.

Conclusion

Both TPUs and GPUs are essential hardware accelerators that enhance computing power, particularly in AI and machine learning tasks. TPUs are designed specifically for deep learning models and excel in power efficiency and matrix operations, making them faster for neural network training. On the other hand, GPUs offer versatility and flexibility, as they can handle a wider variety of tasks beyond machine learning, including graphics rendering and gaming. The choice between GPU and TPU ultimately depends on the specific needs of the user, such as the task at hand, cost, and availability. GPUs are widely available and more affordable, while TPUs are optimized for deep learning but are more expensive and limited to Google Cloud services.

Quiz

Test your knowledge about GPU vs. TPU from the quiz below.