CPU vs GPU vs TPU vs NPU: Key Differences and Use Cases

CPU (Central Processing Unit)

The CPU is the core processing unit responsible for executing instructions and managing the overall operations of a system. It excels at handling diverse, sequential tasks and is the backbone of general-purpose computing.

CPU英文全称是Central Processing Unit，中文全称是中央处理器，是计算机的核心器件，CPU通常由三个部分组成：计算单元、控制单元和存储单元。

• What it Does:
  • Versatile for a wide range of applications, including office tasks and system management.
  • Handles sequential operations efficiently.
  • Integrates seamlessly with other hardware components to ensure task accomplishment.
• Limitations:
  • Not suited for large-scale parallel computations like graphics rendering or AI training.

GPU (Graphics Processing Unit)

The GPU is specialized for parallel processing, making it ideal for handling tasks that involve large-scale computations. Originally designed for rendering graphics, GPUs are now widely used in fields such as gaming, video editing, and AI model training.

GPU全称是Graphics Processing Unit，中文全称叫图形处理器，它也是由三个部分组成：计算单元、控制单元和存储单元。

• What It Does:
  • Processes thousands of tasks simultaneously, enhancing performance in graphics-intensive and AI workloads.
  • Critical for deep learning tasks and scientific simulations.
• Limitations:
  • Consumes more power compared to CPUs.
  • Less effective for sequential, general-purpose tasks.

Found out more about how GPUs work.

TPU (Tensor Processing Unit)

Developed by Google, TPUs are designed specifically for accelerating machine learning workloads. They are optimized for tensor-based computations, which are integral to deep learning algorithms.

• What It Does:
  • Highly efficient for training and inference in machine learning.
  • Designed to work seamlessly with TensorFlow frameworks.
  • Delivers significant energy savings compared to GPUs for specific AI tasks.
• Limitations:
  • Limited versatility; focused mainly on machine learning tasks.
  • Requires compatibility with TensorFlow, restricting flexibility.

Find out more about how TPUs work.

NPU (Neural Processing Unit)

The NPU is a processor tailored for on-device AI tasks, commonly found in smartphones and IoT devices. It accelerates real-time AI computations, such as image recognition and natural language processing.

• What It Does:
  • Extremely energy-efficient for AI inference tasks.
  • Enables on-device processing, reducing latency and reliance on cloud computing.
• Limitations:
  • Task-specific; not suitable for general-purpose computing or AI training.

Key Differences – CPU vs GPU vs TPU vs NPU

When to Use Each Processor

CPU: Versatility in Everyday Computing

CPUs are indispensable for everyday computing, including web browsing, document editing, and managing hardware components. They handle sequential tasks efficiently and act as the central coordinator for other components.

Example: A CPU ensures your operating system and applications work seamlessly together to achieve normal day to day personal and work related tasks.

GPU: Performance for Graphics and AI

GPUs are the preferred choice for applications requiring extensive parallel processing. They excel in rendering high-quality visuals and accelerating AI training processes.

Example: A data scientist training a neural network uses a GPU to process large datasets and achieve faster results. Similarly, a video editor benefits from a GPU’s ability to render high-resolution content quickly.

TPU: Efficiency for AI Model Training

TPUs are optimized for machine learning workloads, particularly when working with TensorFlow-based models. They are ideal for both training large datasets and performing inference tasks efficiently.

Example: A technology firm deploying a language translation AI uses TPUs to train the model, achieving faster results with lower energy consumption compared to GPUs.

NPU: Real-Time AI in Mobile and IoT

NPUs are specifically designed for real-time AI computations in low-power environments. They enable AI features such as facial recognition, voice assistants, and image processing on mobile devices.

Example: Your smartphone uses its NPU for real-time image enhancement when capturing photos or for biometric authentication like face unlock.

How These Processors Work Together

In modern systems, these processors often work in tandem to maximize performance:

1. CPU: Manages overall system operations and task allocation.
2. GPU: Handles intensive workloads like rendering or deep learning tasks.
3. TPU: Optimizes AI training and inference for large-scale models.
4. NPU: Enables efficient on-device AI processing for quick and private computations.

Storage Integration: Pairing these processors with an SSD ensures fast data retrieval and minimizes delays in tasks like loading large datasets or running complex applications.

Real-World Applications

• Gaming:
  • CPU: Processes game logic and interactions.
  • GPU: Renders high-quality graphics.
  • SSD: Reduces loading times for assets and levels.
• AI Research:
  • CPU: Allocates tasks and manages resources.
  • TPU: Accelerates model training.
  • SSD: Provides quick access to large datasets during computations.
• Smartphones:
  • CPU: Coordinates system operations.
  • NPU: Executes real-time AI tasks like voice recognition and image processing.

Also Read: What are AI Chips and How are they Different from Traditional Chips?

Conclusion

Selecting the appropriate processor depends on your specific needs:

• CPU: Best for general-purpose tasks like running applications and managing hardware.
• GPU: Ideal for graphics-intensive tasks, AI training, and video editing.
• TPU: Suited for large-scale AI training and inference, particularly in TensorFlow.
• NPU: Designed for efficient, real-time AI tasks on mobile and IoT devices.

CPU Vs GPU Vs TPU Vs NPU: Key Differences And Use Cases | Appscribed

APU — Accelerated Processing Unit, 加速处理器，AMD公司推出加速图像处理芯片产品。

BPU — Brain Processing Unit, 地平线公司主导的嵌入式处理器架构。

CPU — Central Processing Unit 中央处理器， 目前PC core的主流产品。

DPU — Deep learning Processing Unit, 深度学习处理器，最早由国内深鉴科技提出；另说有Dataflow Processing Unit 数据流处理器， Wave Computing 公司提出的AI架构；Data storage Processing Unit，深圳大普微的智能固态硬盘处理器。

FPU — Floating Processing Unit 浮点计算单元，通用处理器中的浮点运算模块。

GPU — Graphics Processing Unit, 图形处理器，采用多线程SIMD架构，为图形处理而生。

HPU — Holographics Processing Unit 全息图像处理器， 微软出品的全息计算芯片与设备。

IPU — Intelligence Processing Unit， Deep Mind投资的Graphcore公司出品的AI处理器产品。

MPU/MCU — Microprocessor/Micro controller Unit， 微处理器/微控制器，一般用于低计算应用的RISC计算机体系架构产品，如ARM-M系列处理器。

NPU — Neural Network Processing Unit，神经网络处理器，是基于神经网络算法与加速的新型处理器总称，如中科院计算所/寒武纪公司出品的diannao系列。

RPU — Radio Processing Unit, 无线电处理器， Imagination Technologies 公司推出的集合集Wifi/蓝牙/FM/处理器为单片的处理器。

TPU — Tensor Processing Unit 张量处理器， Google 公司推出的加速人工智能算法的专用处理器。目前一代TPU面向Inference，二代面向训练。

VPU — Vector Processing Unit 矢量处理器，Intel收购的Movidius公司推出的图像处理与人工智能的专用芯片的加速计算核心。

WPU — Wearable Processing Unit， 可穿戴处理器，Ineda Systems公司推出的可穿戴片上系统产品，包含GPU/MIPS CPU等IP。

XPU — 百度与Xilinx公司在2017年Hotchips大会上发布的FPGA智能云加速，含256核。

ZPU — Zylin Processing Unit, 由挪威Zylin 公司推出的一款32位开源处理器。