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[AI CHIP, NPU] What is NPU? (2024)

[AI CHIP, NPU] What is NPU? (2024)

Tags
NPU
AI
Infra
Wrote
2024.11
Currently I’m working at Startup based on AI Technology. Recently, I am focusing on research project that making NPU usage more easily.
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notion image
As an exhibitor at NexTech Tokyo 2024 and KES 2024 in Seoul, I encountered a common question from visitors:
"I know about CPUs and GPUs, but what exactly is an NPU?"
This inspired me to explain NPUs in a straightforward way, focusing on their significance in the future of AI technologies.
 

What is an NPU?

The term "NPU" (Neural Processing Unit) may be more widely recognized in Korea than in other countries—at least, that’s my impression.
NPU is a specialized AI chip designed for AI inference tasks. Compared to GPUs of similar size and specifications, NPUs can deliver faster inference speeds while consuming less power—a critical advantage for many applications.
For instance, I’ve worked with Korean NPUs such as the "Warboy" from FuriosaAI and the "X220" from SAPEON, both of which demonstrated impressive performance in my tests.
 

Why Choose an NPU Over a GPU?

Let’s take a practical example:
Imagine someone wants to use AI to monitor CCTV footage for detecting specific objects or situations. They train a computer vision model using a GPU, achieve great results, and decide to deploy this service in the real world.
Initially, they use the same GPU to run the service 24/7. But soon, they realize they also need to train additional models while maintaining uninterrupted service.
In such scenarios, a chip specialized in inference, like an NPU, becomes invaluable. It enables efficient, continuous service without monopolizing resources needed for training.
Some may argue that a small, affordable GPU could work just as well. While that’s true today, NPUs have the potential to become more cost-effective with mass production. Once NPUs become widely available and cheaper than GPUs, the adoption of inference-dedicated hardware will likely increase dramatically.
Moreover, with the vast repository of pre-trained models available on platforms like Hugging Face and GitHub, not every engineer needs to train models from scratch. This trend further amplifies the value of inference-specific hardware.
 

How Can I Use an NPU Easily?

Currently, I’m working on simplifying NPU adoption by developing tools and applications that streamline the process.
Using an NPU for inference often involves several steps like calibration, quantization, and model conversion. To make this easier, I’ve created an automated workflow accessible through a web-based UI, enabling users to leverage NPU capabilities with minimal effort.
 

Conclusion

As AI continues to evolve and integrate into our daily lives, efficient and scalable inference will play a pivotal role in unlocking its full potential. NPUs, with their ability to deliver high performance while reducing power consumption, represent a significant step forward in this journey.
While GPUs have been the cornerstone of AI development for years, NPUs are emerging as the future of inference technology, especially as their cost and accessibility improve. For industries and individuals looking to deploy AI applications effectively, understanding and embracing NPU technology will be essential.
Through my work, I aim to make NPUs more accessible, ensuring that anyone—from engineers to business leaders—can harness their power without unnecessary complexity. With tools and platforms that simplify NPU adoption, I believe we can pave the way for a more sustainable and innovative AI ecosystem.