CPU vs GPU vs TPU vs NPU

- Overview

CPUs, GPUs, and TPUs are the foundation of modern computing. First came CPUs, then GPUs, and now TPUs. As the tech industry grows and finds new ways to use computers, so does the need for faster hardware.

• CPU: Your go-to for everyday tasks, office work, and browsing. Great for general-purpose tasks, but might struggle with heavy graphics or AI
• GPU: Essential for gaming, graphic design, and video editing. King of graphical tasks but may not be as efficient for general computing.
• TPU: Vital for AI research, data analysis, and deep learning. King of graphical tasks but may not be as efficient for general computing.
• DPU: Revolutionizing data centers, enhancing security, and optimizing network performance. Emerging as the powerhouse for data center optimization.
• NPU: An artificial intelligence (AI) accelerator, also known as an AI chip, deep learning processor or neural processing unit (NPU), is a hardware accelerator that is built to speed AI neural networks, deep learning (DL) and machine learning (ML).
   

Understanding CPU, TPU, GPU, DPU, and NPU may seem like a maze, but have no fear! Each has its own unique advantages and purpose, making them indispensable in the technology ecosystem. Your choice ultimately depends on your specific needs, whether it's personal equipment or data center optimization.

Please refer to the following for more information:

• Wikipedia: AI Accelerator

- AI Accelerators

An artificial intelligence (AI) accelerator, also known as an AI chip, DL processor, or neural processing unit (NPU), is a hardware accelerator designed to accelerate AI neural networks, deep learning, and machine learning.

As AI technology expands, AI accelerators are critical to processing the large amounts of data required to run AI applications. Currently, AI accelerator use cases cover smartphones, personal computers, robots, autonomous vehicles, Internet of Things (IoT), edge computing, etc.

For decades, computer systems have relied on accelerators (or coprocessors) to perform a variety of specialized tasks. Typical examples of coprocessors include graphics processing units (GPUs), sound cards, and video cards. 

But with the growth of AI applications in the past decade, traditional central processing units (CPUs) and even some GPUs can no longer handle the large amounts of data required to run AI applications. 

AI accelerators have specialized parallel processing capabilities and can perform billions of operations simultaneously.

- CPU vs. GPU vs. TPU vs. NPU

The difference between a CPU, GPU, TPU, NPU is that the CPU handles all the logic, calculations, and input/output of the computer, and it is a general-purpose processor. In contrast, a GPU is an additional processor used to enhance the graphics interface and run high-end tasks. TPUs are powerful custom processors that can run projects made on a specific framework (i.e. TensorFlow). NPUs are hardware accelerators that are built to speed AI neural networks, DL and ML.

Here are some differences between CPUs, GPUs, TPUs, and NPUs: 

• CPUs: Central Processing Units manage all the functions of a computer. CPUs perform computations sequentially and are used in nearly every computer device.
• GPUs: Graphical Processing Units improve the graphical performance of a computer. GPUs are often used in gaming PCs and excel at parallel computing and graphics-intensive tasks.
• TPUs: Tensor Processing Units are custom-built ASICs that accelerate TensorFlow projects. TPUs are designed specifically for machine learning and can provide faster performance for certain tasks, especially those that involve large matrix multiplications. TPUs are well suited for Convolutional Neural Network (CNN), while GPUs have benefits for some fully-connected Neural Networks, and CPUs can have advantages for Recurrent Neural Network (RNN)s.
• NPUs: A neural processing unit is a class of specialized hardware accelerator or computer system designed to accelerate AI and machine learning applications, including artificial neural networks and machine vision. Typical applications include algorithms for robotics, Internet of Things, and other data-intensive or sensor-driven tasks.

CPUs offer versatility and single-threaded performance, GPUs excel at parallel computing and graphics-intensive tasks, and TPUs are the go-to choice for accelerating machine learning and deep learning workloads.

TPUs' hardware is specifically designed for linear algebra, which is the building block of deep learning.

DPUs are the new kids on the block! They’re all about optimizing data center workloads, handling networking, security, and storage tasks efficiently. Think of them as the ultimate multitaskers for the data center world.

- AI Chips: NPU and TPU

A Neural Processing Unit (NPU) is a microprocessor designed to accelerate machine learning algorithms, typically by running on predictive models such as Artificial Neural Networks (ANN) or Random Forests (RF). It is also known as a neural processor.

Note that it cannot be used for general-purpose computing such as a central processing unit (CPU). This is mainly because no software support has been developed for such processors for any computing purpose. In fact, developing such a software/compiler can be a challenge, and at the same time, it may provide low performance for non-design tasks.

Neural Processing Units (NPUs) and Tensor Processing Units (TPUs) are specialized hardware accelerators designed to accelerate machine learning and artificial intelligence (AI) workloads. NPUs and TPUs are optimized for mathematical operations commonly used in machine learning, such as matrix multiplication and convolution, and they can be used to accelerate a variety of machine learning tasks, including image classification, object detection, natural language processing, and speech recognition.

[CPU vs GPU vs TPU - Linkedin]

- Need for NPU

Over the past few years, we've made incredible progress in applying machine learning and outperforming humans in certain tasks, such as playing games like Go and Chess. At the same time, machine learning applications are taking human life to the next level. 

Some applications include:

• Self-driving car
• Monitoring systems or areas from threats, such as security systems involving real-time facial recognition
• Improving Healthcare Through Accurate Analysis and Treatment
• and Many others

All of this increases the amount of computation involved exponentially, and previous methods using GPUs do not scale well. This paves the way for designing processors that outperform GPUs and address the advances we are making in machine learning. 

The NPU is required for the following purposes:  

• Increase the computational speed of machine learning tasks by several times (nearly 10K times) compared to GPU
• Lower power consumption and improved resource utilization for machine learning tasks compared to GPUs and CPUs

Real life implementations of Neural Processing Units (NPU) are: 

• TPU by Google
• NNP, Myriad, EyeQ by Intel
• NVDLA by Nvidia
• AWS Inferentia by Amazon
• Ali-NPU by Alibaba
• Kunlun by Baidu
• Ascend by Huawei
• Neural Engine by Apple
• Neural Processing Unit (NPU) by Samsung

[More to come ...]