NPU vs. GPU: the Difference

The incorporation of artificial intelligence (AI) into the mainstream, the neural processing unit (NPU) has become important when purchasing a PC, but knowing the difference between it and the GPU is important.

The world of processors can be a complex one, but when it comes to NPUs and GPUs, there's a clear distinction in their specialties. This guide dives into their functionalities and why both play crucial roles in modern devices.

NPU vs. GPU: the Difference
The NPU: Master of Machine Learning

• What it is: An NPU (Neural Processing Unit) is a specialized processor designed to accelerate tasks related to artificial intelligence (AI) and machine learning (ML).
• Key Features:
• Hardware Optimization: Built with specific hardware in mind, NPUs excel at performing AI and ML computations efficiently while conserving energy.
• Parallel Processing Power: Similar to GPUs, NPUs can handle multiple operations simultaneously, making them adept at tasks like inference and training AI systems.

Real-World Applications:

• Facial recognition in your smartphone.
• Training AI systems for various purposes.

The GPU: Champion of Graphics

• What it is: A GPU (Graphics Processing Unit) is another specialized processor, but its forte lies in accelerating graphical tasks like image/video processing and rendering.
• Similar Strengths: Like NPUs, GPUs boast:
• Parallel Processing: The ability to execute numerous operations concurrently.
• High Throughput: GPUs excel at handling data-intensive tasks like large-scale data processing and complex calculations (cryptocurrency mining).
• Beyond Graphics: While initially designed for graphics, GPUs are now used for a wider range of tasks:
• Training large neural networks (used in large language models like me!).
• Data-intensive scientific simulations.

The Key Difference: Specialization Matters

The critical distinction lies in their focus:

• NPUs: Dedicated to accelerating AI and ML workloads.
• GPUs: Specialize in graphics processing and rendering, but have expanded into other computational areas.

Why Both Exist? Efficiency Reigns Supreme

Though GPUs can handle some AI tasks, NPUs offer advantages:

• Performance: NPUs are often designed specifically for AI, leading to potentially faster performance for these tasks.
• Efficiency: NPUs can achieve high performance while using less power compared to GPUs for similar AI workloads.

Hardware Acceleration: A Winning Team

Using dedicated processors for specific tasks is known as hardware acceleration. It improves overall performance by having components optimized for particular functions. Just like having a dedicated GPU for graphics rather than relying solely on the CPU.

Measuring Performance: A Race of Trillions

Both GPU and NPU performance is measured in TOPS (Tera Operations Per Second). This reflects how many trillions of operations the processor can perform in a second.

Physical Form Factors: Integrated or Separate

• GPUs: Can be discrete (separate card) or integrated (built into the CPU).
• NPUs: Currently, most NPUs are integrated into the CPU (e.g., Apple's A-Series and M-Series chips). However, some discrete NPUs exist (e.g., Raspberry Pi NPU hat).

NPUs are the AI specialists, while GPUs are the graphics champions, both wielding the power of parallel processing. While NPUs are currently focused on AI, GPUs have become more versatile, venturing into other computational domains. Understanding these specialists empowers you to see the inner workings of your devices and appreciate the technology driving innovation.

This is everything you need to know about NPU vs. GPU: the difference, we hope we have been very helpful for you to understand the difference.