What Are NPUs and Why Should You Care

Your phone gets an NPU, your computer gets an NPU, everyone gets an NPU - Nueoprah

As if just learning about CPUs weren’t enough, today we have to move on to the next PU.

Enter NPU - something that will pretty soon power most of everything around you.

I get it; you are confused right now, and just like every other new technology that enters the market, this one also seems like it's out of this world.

You probably don’t know anything about it yet, and my guess is that is why you are here today, right? We will touch on that topic later on; before that, I need to give you a background to this technology first.

So, gentle humans, you must have noticed that AI is everywhere now. From your chatbots to your photo editors and even your phone's camera. Quite literally, everything’s getting smarter. But the real question to be asked is: Who is doing the heavy lifting behind the scenes? Is it Captain America? Or maybe it is the hardware.

For the longest time, it was all about CPUs and GPUs. Everyone is familiar with the fact that CPUs handle basic tasks while GPUs take care of the heavy graphics stuff. But recently, a new player just entered the battlefield, which is NPU, and everyone is probably wondering what in the world it is, right?

Well, as we are aware, GPUs were designed to perform activities that involved graphics-intensive work, but quite similarly, NPUs are built specifically for handling AI tasks. These chips are much faster, more efficient, and can handle machine learning models without burning your device to ashes.

My goal today is really straightforward: It is to explain what NPUs are, why they are needed, how they work, and this is not it; we will be talking about its use cases and tons of other stuff related to it as well. So, enough talking, and let's get started.

What the Heck is an NPU?

Alright, I have asked the question “What exactly is an NPU?” tons of times, and you might be pulling your hair at this point while screaming, “Just tell me what it is”, so let's finally do that.

NPU stands for neural processing unit. It’s a special kind of processor that’s optimized for AI and machine learning tasks. It’s specifically designed to handle things like deep learning and neural networks way faster and more efficiently than a CPU or GPU ever could.

The name comes from the fact that AI models use neural networks. Eh, but what is a neural network? A neural network is, in layman’s terms, a vast mesh of interconnected nodes that pass information between them. (The whole idea was modeled after the way our own human brains work.)

NPUs are basically built to think similarly to how a human would think, processing massive amounts of data and recognizing patterns, which is exactly what AI models need. To simplify it even further, here is a list that will help you understand NPUs even more.

- CPU = Brain of the computer
- GPU = King of graphics and complex compute
- NPU = The AI Specialist

One important thing to note is that an NPU isn’t a separate device that you buy and plug in (as you would with a GPU). Instead, an NPU is packaged as part of a modern processor platform, such as:

- Intel’s Core Ultra
- AMD’s Ryzen AI
- Qualcomm’s Snapdragon X Elite

These platforms have a CPU along with an integrated GPU and NPU.

Why Do We Even Need NPUs?

Alright, so we now know what an NPU is, but I am quite sure everyone must have questions such as “Why were they even created?” or “Why do we even need them in the first place?” lurking in their head. And the answer is quite simple.

“AI became too powerful for the hardware we had.”

Back in the day, CPUs handled everything. Then, GPUs came in and made things faster with parallel processing. But with time, AI models became more advanced, and due to that, even GPUs started struggling to keep up. Why? Well, because you see, AI isn't like your regular coding tasks. It involves running deep learning algorithms and processing insane amounts of data in real time, which can be very hardware-demanding, and neither CPUs nor GPUs are built to handle AI.

Some of you might still be clueless and wondering what exactly the problem is. Well, the problem is that CPUs are simply too slow to handle the mighty AI. On the other hand, GPUs are too power-hungry and inefficient for constant AI tasks.

To understand this problem even better, let's use an analogy. Imagine AI as Thor’s hammer, which is insanely powerful, but not just anyone can wield it. CPUs and GPUs were simply not worthy enough to wield their power. That’s where NPUs come in, built specifically to lift this hammer and unleash the full power of AI.

I am sure that you are starting to realize why we needed this technology in the first place and why it was created. NPUs exist because AI is evolving too fast for normal hardware to handle. And with AI getting integrated into everything, whether it's your smartphone or the self-driving car that is not parked in your garage. We needed dedicated hardware that could keep up with AI, which is an NPU.

How Do NPUs Work?

We have already come quite far. Just a few minutes ago, we were confused about what NPUs are, and now we will be learning about how they work. You must be curious at this point to know how they work, right?

Okay, so imagine that you are running in a race, but you are not alone. Thousands of your clones are running beside you as well, each handling a small part of the race. That's exactly how NPUs handle AI tasks in a nutshell, but there is still much more to the story, so how about we actually get to that story? Let’s get going

Inside the Brain of an NPU

At the very core, NPUs (Neural Processing Units) are built on something called parallel computing architecture. At first, this term seems like it is out of the world, but what it simply means is:

“A kind of architecture that allows NPUs to perform multiple AI tasks at the same time.”

Unlike a CPU, which processes instructions sequentially (one step at a time), or a GPU, which handles multiple tasks but is optimized for graphics, NPUs are specifically designed to handle neural network operations. But how does NPU do this? All credit goes to some of the core abilities that allow NPUs to complete different AI tasks. What core abilities? They are listed below:

- Parallel Computing: NPU doesn’t process one task at a time. Instead, it can handle thousands of small AI tasks at once, and all this is possible because of parallel computing.

- Dedicated AI Cores: Inside an NPU, there are special cores that are built only for AI operations. This allows NPUs to process massive amounts of data without wasting power.

- Low Power, High Efficiency: While GPUs burn power like crazy, NPUs are optimized to deliver high performance with minimal energy consumption.

How an NPU Handles Different AI Tasks

An NPU is the one pulling the strings behind all that AI magic. It not only processes machine learning models but also handles inference like it's child's play. Lastly, it is also responsible for accelerating the neural network. You might be wondering, “What in the world are all these gibberish terms?” Let’s break them down and dive a bit more into the rabbit hole.

Processing Machine Learning Models

When an AI model is trained, it’s like teaching a machine how to think. You teach that machine how to recognize patterns, how to make decisions, and most importantly, how to understand data. But here’s the thing…Training is just the first step. The real challenge begins when that model has to work in real time in order to detect things in a photo or analyze speech, and that’s where the NPU steps in.

Unlike CPUs and GPUs, NPUs are built very differently. They process multiple AI operations in parallel (side by side), which allows them to handle thousands of tiny tasks at lightning-fast speed while consuming minimal power.

Handling Inference

Training an AI model is just like teaching a student. But after it’s done being trained, then what? That’s when the student steps into the real world and starts making decisions on their own. That is exactly what Inference means, it’s the process where the AI model actually starts making its own decisions after it's been trained.

This process is very important. Think about it- whenever your phone identifies your face, when ChatGPT responds to your text, or when a self-driving car makes a decision, who's doing all that work? It’s inference!

But here’s the problem...CPUs and GPUs, yeah, they exist, but they’re like boomers trying to keep up with TikTok trends. Slow and inefficient. This is where our boy NPU steps in again. The NPU runs these AI decisions quite easily without any lag, without draining your battery, and without setting your device on fire.

Neural Network Acceleration

I know the term “Neural network” sounds fancy, but in reality, it's just math pretending to be a human brain, and matrix multiplication is the heart of all this AI magic. And guess what? CPUs and GPUs suck at it.

Once again, our savior, NPU, steps onto the battlefield and crushes these calculations at insane speeds. They analyze massive data sets, spot patterns like a detective, and provide us with accurate results in milliseconds.

When you're using Google Lens to translate text in real-time or Snapchat's AI filters to track your face, that's neural network acceleration in action. In simple words, an NPU is the reason your AI feels instant and smooth.

NPUs vs GPUs vs CPUs – What’s the Difference?

Okay, I am quite sure that by now, you know that NPUs are the new king when it comes to AI. But in order to really understand why NPUs dominate, we gotta break down the different technologies such as CPUs, GPUs, and NPUs, and compare them side by side as well. This will reinforce so much of our understanding when it comes to NPU, and trust me, once we unravel these three, it'll all make perfect sense. So, let’s get right into it.

CPU: The Jack of All Trades

The CPU (Central Processing Unit) is like the all-rounder of your computer. It’s the brains behind everything. Whether you are running your apps, browsing the internet, or quite literally doing any task, the CPU is helping you do it, and it’s great at it.

But here's the catch: CPU is not built to perform tasks in a very speedy way. It performs tasks one after the other, which gets the general work done, but when it comes to handling AI, the CPU cannot get the job done because there is a need to process thousands of tiny operations at once.

This is why CPUs struggle with deep learning and neural network tasks. They're just not built for it.

GPU: The Graphics Beast

Ah, yes, the GPU (Graphics Processing Unit). The famous knight that is burdened with the responsibility of making your games run smoothly. Different questions will be lurking in your mind, including this:

“So…even GPUs aren’t powerful enough to handle AI?”

GPUs are indeed powerful enough to handle AI if that is what you are wondering. They aren’t just limited to gaming. They have parallel processing ability, which makes them a popular choice for AI tasks, too. Unlike CPUs, GPUs can handle thousands of operations at once, which makes them way faster for deep learning and machine learning models.

“If so, then why not simply use GPUs?”

Sure, people can use GPUs for handling AI tasks if they want to go broke while paying their electricity bills. Yep, that's right! They consume too much power. While GPUs can handle AI, they weren’t built for it. They are too power-hungry, and not only that, they heat up really fast and drain your battery like crazy as well.

So yeah, great for running Cyberpunk 2077 at ultra settings, but it's not ideal for efficient AI work.

NPU: The AI Specialist

Alright, you’ve heard me say NPU a hundred times now, but let’s make things crystal clear once and for all. So, NPU (Neural Processing Unit) is built from scratch for AI tasks and doesn’t care about anything other than AI tasks. It’s like that one guy in the gym who only trains his upper body (like me) and doesn’t care about any of the other muscle groups. You might be wondering:

“What makes an NPU so special, right?”

Actually, there are tons of things that make NPU special. Let me touch on some of the key points from a higher pov since we have already covered them in detail. So, what key points am I talking about? The following:

- It handles machine learning inference and neural network acceleration with ease.
- It can process thousands of AI tasks in parallel.
- It consumes way less power, so your device doesn’t heat up or drain the battery.
- It is perfect for on-device AI tasks like face unlock and voice recognition, and it can also do object detection in real time.

In simple words, where CPUs and GPUs struggle, the NPU shines.

Comparison Table

Comparison tables personally help me a lot in pointing out the differences between things, so I thought I would provide you guys with a table as well. Following is a table that basically summarizes all the key differences I covered above.

Where Are NPUs Being Used?

So, I think we have talked enough about how the NPUs themselves work, but where are they actually being used? Are they even being used?

Well, let me tell you something. As we all know, AI is spreading like wildfire, and handling it with traditional hardware is nearly impossible. That's why NPUs are being quietly integrated into almost everything around us. Whether it’s your smartphones or laptops, cloud servers, or even drones, NPUs are slowly becoming a part of everything.

Why? Because AI needs something powerful enough to handle its insane workload, and NPUs are built exactly for that. Now, let's dive into the details of where exactly they are being used.

Consumer Electronics (Smartphones, Laptops, & PCs)

Many of today’s premium smartphones, laptops, and tablets come with a built-in NPU. You can think of NPU inside different devices as the infinity stones in Thanos's hands. They grant him superpowers, right? Just like that, NPU also enables these devices to perform many AI tasks very efficiently. Tasks such as:

- Real-time image and video editing, like blurring the background during video calls.
- Voice assistants that can understand and respond to your commands.
- Smart camera features that include detecting faces and stabilizing shaky videos.

Data Centers and Cloud AI

Ever wondered how Netflix recommends your favorite shows to you and how Amazon suggests products according to your taste? It’s all because of powerful NPUs that are directly attached to server motherboards in large data centers. They accelerate recommendation engines due to which all this is possible; other than that, they are also responsible for generating text and images in real-time.

Edge Devices and IoT

NPU plays a very important role in edge devices and IoT devices, where real-time processing is crucial. The term edge devices might seem somewhat complex but it can simply be defined as:

“The hardware devices that are able to process data locally, instead of sending it to a cloud server for processing.”

The reason why NPU is used in edge devices and different IoT devices is that it allows different devices to handle AI tasks in real time without depending on the cloud. Like, think of it: devices performing tasks locally? It's a game changer for modern tech because things get much faster and more secure when processing things locally. Some key areas where NPUs are making a big impact are as follows:

- Sensors that monitor machines in real time.
- Drones that make decisions while flying.
- Self-driving cars that detect objects in the way and..drive safely of course.

Robotics

You thought this was it? Nope! NPU’s got their hands even in the robots, not talking about the terminators of course. I am talking about robots that are used in different industries to speed up tasks. NPUs help robots think faster and act smarter. They allow robots to make quick decisions, for example:

- Warehouse bots are hustling and sorting packages like pros.
- Surgical machines cutting with insane precision (finally..no more shaky hands).
- Delivery bots dodging obstacles like they’re in some zombie video game.

The Future of NPUs and AI

All of you must be waiting for this part, I am sure of that. But let me give you a heads up that I am not Doctor Strange, who can look into a portal and see the future. I can only present you with data based on facts, and sometimes things can go wrong even if they seem like they won’t, so with that being said. Let’s get to the point.

So, you might be thinking that NPUs are at their limit because they are already changing so many things in the world, but here is the thing: NPUs are just warming up. The world does have hardware that is powerful enough to handle NPUs, and the potential is massive, but the software? It is still catching up. At present, there are simply not enough software applications that are built to squeeze every drop of power out of NPUs, at least not yet.

Now, don't get sad about the fact that there isn’t software powerful enough to handle NPUs because this is temporary. With the passage of time, AI-powered PCs are going to take over the world, and guess what? Developers won’t be ignoring that. Like, why would they not want to take advantage of the extra horsepower just standing at their doorsteps? They are definitely going to build, optimize, and push NPUs to do what they were made for in the first place. The interesting is that:

“All this is not a matter of if rather, when this will happen.”

And when that happens, Everything changes.

Oh boy, talk about Security. Now that’s where things start to get really interesting, not just because I love computer security, but also because with the evolution of tech, security is becoming more and more necessary. McAfee is already using NPUs to detect deepfakes.

We also have edge computing that is becoming better with each passing day. Think about it: NPUs handling AI tasks locally. Take a shot at what that means. It means your data stays where it belongs: on your device. You don’t have to upload your data onto the cloud, and on top of that, you don’t even have to wait to send data to a server that is miles away.

“In a world where cyber threats evolve by the second, it’s a necessity.”

And, let's not forget that NPUs are handling heavy AI workloads without turning your device into a space heater. Remember GPUs? And do you also remember the part where GPUs escaped the gaming world and took over AI? I hope you do because NPUs are about to do the same thing.

Just give it a little more time, and you will see that NPUs will be running everything. Whether it’s real-time language translation, massive AI models in data centers, or even the AI that decides what ad to shove in your face next, it will be pulling the strings to everything while staying in the shadows. (And no, terminators are still not coming)

So, yeah... AI is only going to get bigger with time, and NPUs will be the silent engine driving it all. An interesting future ahead, isn’t it?

Bottom Line: Should You Get a PC With an NPU Now or Wait?

Alright, I am sure all of you must have this question. That is why I decided to cover this as well. What question? Whether or not you should buy a PC or a Laptop with having NPU. So, instead of saying a simple yes or no, let me dive a bit deeper. That way, it will be really helpful for you to make a decision.

If you personally ask me that question, then I would say that right now, NPUs aren’t really a must-have, but as time progresses, NPUs are becoming more common in modern laptops. So… If you are planning to buy a new laptop, chances are it will have an NPU anyway. And on top of that, it will also have other improvements like better battery life. But the real question is:

“Should you go out of your way to get one? Not necessarily.”

We also got desktops. Did you think I forgot about them? Nope! I do have good news for the tech wizards: both AMD and Intel now have desktop chips with NPUs. AMD has the Ryzen 8000 G-Series, and Intel has the Core Ultra 200 series.

So, if you're one of those guys who loves future-proofing your setup, I get it; getting an NPU-equipped desktop makes sense. But for the average Bashir, it is not really a necessity.

Also, let’s not forget that, as of now, the majority of AI tools, like GPT, are still running on the cloud. So, would an NPU really make that much of a difference? Nope! But NPUs will definitely play a bigger role in the upcoming days.

Final Thoughts

If you do end up finding a good deal on a non-NPU laptop or desktop, don’t bash your head into the wall thinking about whether or not you should get it. Simply ask yourself if your use case has applications that can benefit from the NPU or not. And if you really want to go out of your way to buy a device with an NPU, then just do it.

“Just remember, NPUs are not a must but a nice-to-have.”

Other than that, today was a special day. When you first clicked on this article, you weren’t even aware of what an NPU is, and now? You got down all the basics. We covered what NPUs do, where they’re used, and most importantly, whether you should buy a device with one. The short answer? It’s nice to have, but not a must-have…at least not yet. And regarding this question, I am sure I have armed you with enough knowledge that you can make the big call for your own self.

Lastly, if this article even slightly helped you understand the tech itself or decide whether to grab an NPU-powered device, let me know about that by dropping a comment, because I actually read them.

Further Learning

• What the heck is an NPU, anyway? Here's an explainer on AI chips | PCWorld

• The AI PC revolution: 18 essential terms you need to know | PCWorld

• What is Microsoft Copilot? Here’s everything it can do | Tom's Guide

• What is an NPU? Neural Processing Unit explained. - Pureinfotech

• Do we really need NPUs now?

• The NEW Chip Inside Your Phone! (NPUs)

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