What is an NPU? A simplified guide to get you started
- Mobilint Admin
- Apr 4
- 4 min read
Updated: Apr 7
We can’t talk about AI without tackling its three tenets: Model, data, and the processor.
To start our blog, we’ll explore what a neural processing unit (NPU) is, why it matters, and how it impacts the real world.
What is an NPU?
An NPU, or neural processing unit, is specialized hardware optimized for artificial intelligence and machine learning tasks. As the name suggests, it is designed to effectively process operations like neural networks, where a vast network of nodes exchanges and processes information.
While NPUs are not exactly new, they’ve gained significant traction with the rise of AI. The NPU’s architecture and nature of operation are dedicated specifically for AI tasks, allowing them to process workloads more efficiently and with lower power consumption than general-purpose processors like GPUs or CPUs.
What can an NPU do?
NPUs are designed to run AI models efficiently. Being significantly energy-efficient than GPUs and more effective for AI workloads than CPUs, NPUs answer the growing demand for portable, independent AI solutions, with which users don’t have to rely on cloud or internet connectivity. For this reason, NPUs are now integrated into mobile devices like phones or laptops.
So, can NPU replace GPU?
Not entirely, but they complement each other. GPUs are still one of the most well-known options for maximized performance required in heavy and intensive AI workloads, like training large models.
However, because GPUs are very power-hungry and bars are kept high with a steep pricing range, the best option is to delegate tasks that don’t require a GPU to NPUs instead. And what many people don’t realize yet is that they can offload more tasks than they think!
A great example is when it comes to simply running deep learning models that are ready to use. For these AI inference tasks, NPUs aren’t just cheaper but also can be faster and better, all the while consuming significantly less power and staying cool.
Advanced NPUs provide support for a wide variety of AI models (including state-of-the-art ones). Soon, we're set to publicly open our Model Zoo, where we list hundreds of open-source deep learning models, all tested to run successfully on our NPUs. The list includes vision models like YOLO, as well as transformer-based models like large language models. Of course, users can also opt to bring their own private models.
CPU vs. NPU vs. GPU
Because each processor serves a different purpose, they are not direct replacements for one another. The table below lists some of the key differences between the three hardware products.
Feature | NPU | GPU | CPU |
Purpose | Optimized for AI and deep learning workloads | Designed for graphics rendering, also used for AI | General-purpose processing for all computing tasks |
Architecture | Specialized for neural networks and matrix operations | Parallel processing for vector/matrix operations | Serial processing with few cores, optimized for diverse tasks |
Performance Unit | Measured in TOPS (Tera Operations Per Second) | Measured in FLOPS (Floating Point Operations Per Second) | Measured in Hz & IPC (Instructions Per Cycle) |
Energy Efficiency | Very high efficiency for AI workloads (high TOPS/Watt) | Moderate efficiency, high power consumption | Least efficient for AI workloads |
Primary Use Cases | AI inference on edge devices, robotics, mobile, smart assistants | Gaming, AI training, deep learning acceleration | General computing, operating systems, applications |
For running AI models, NPUs would be the best option. GPUs are good for training AI. CPUs would be better suited for general computing tasks rather than AI workloads.
How does an NPU work?
An NPU solves trillions of mathematical operations (like adding and multiplying) simultaneously each second, wherein each of these equations are the broken-down bits of a deep learning model. This is why the performance of NPUs are measured by “TOPS,” which stands for “tera (trillion) operations per second.” In the process of simplifying and solving these equations, an NPU is responsible for finding the sweet point between speed, efficiency, and accuracy.
Putting it in scale, ARIES, our AI accelerator, performs at 80 TOPS. This range equals to processing 11,551 frames per second (FPS) on a MobileNetV2 model, used to detect objects, under specific benchmarking conditions.
Computational efficiency unlocked by algorithmic research is key to making NPUs work, together with advanced hardware architecture and user-friendly software stacks. Some NPU companies opt to specialize in one of them; But here at Mobilint, we believe they must be co-optimized as one to fully realize their potential.
Why NPU matters
AI is reshaping the world the way mobile phones and the internet did in the past, and it’s also here to stay. AI can do dangerous, tedious, and manual work so humans can focus on the things that matter. It’s bringing previously unseen innovations to every industry on earth.
But it comes with another edge to its sword. Resources to develop and use AI are scarce, and it’s creating an imbalance among those who have access and those who don’t. With NPUs, more institutions can harness AI technology with less dependence on expensive hardware in the market.
Moreover, as an NPU processes models on-device, a system that carries an NPU is fully independent. This independence is critical to unlocking innovations in autonomous systems like robotics, drones, and aerospace applications.
In addition to this, in the bigger picture, NPUs also help reduce the environmental impact of using AI by using less energy to produce the same output.
Industry use cases of NPU
NPUs are deployed across every industry where AI/ML can make an impact. Aside from mobile devices, including PCs and cell phones, here are some examples of where you can find them:
Security
A. Real-time threat detection
B. AI/ML-enhanced cybersecurity
C. Drones
D. Video data analysis
Smart Cities
A. Traffic control
B. Public safety monitoring
Manufacturing
A. Quality control
B. Collaborative robots
C. AI/ML-powered digital twins
D. Supply chain management
E. Workplace safety monitoring
Retail
A. Automated checkout
B. In-store analytics
C. Personalized marketing
D. Smart kiosks
Wrapping up…
NPUs are relatively new technology, and there’s much more potential to explore. The rise of AI has sped up the propagation of NPUs, but as AI development accelerates, raising further awareness about NPUs is a vital assignment to ensure institutions make informed hardware choices.
At Mobilint, we take this mission seriously. It’s one of the core reasons why we’ve launched our blog—we’re hoping to help more people make sure they’re finding the right hardware solutions when integrating AI.
We’ll be bringing more information to help you propel your AI journey forward. Stay tuned and see you here again soon. ⚡
