NPU in smartphones: what it is, how it works, and why it matters so much

If you've ever wondered how it's possible that your mobile phone Enhance your face in a photo, understand what you say by voice, or remove objects from an image In a couple of seconds, the answer boils down to three letters: NPU. This small block inside the processor has become one of the silent protagonists of the new wave of artificial intelligence in phones.

Today, virtually all modern smartphones have an NPU, but very few people know about it. What exactly does it do, how does it differ from the CPU and GPU, and why does it have such a significant impact on the camera, battery, and privacy?We're going to break down everything behind the "AI brain" of your mobile phone, with clear examples, comparisons, and some key points to help you choose your next phone wisely.

What is an NPU in a smartphone?

The acronym NPU stands for Neural Processing Unit or Neural Processing UnitIt is a type of specialized processor, integrated within the SoC (system-on-chip) of your mobile phone, whose sole purpose is to accelerate the calculations necessary to run artificial intelligence and machine learning models.

While the CPU and GPU are more general-purpose components, the NPU is designed for always perform the same mathematical operations, but in a massive way and in parallel.: matrix multiplications, convolutions, and other calculations using deep neural networks.

Neural networks are the foundation of modern AI: a layered structure with millions of parameters that It receives input data (text, images, voice), processes it, and generates an output.such as a translation, an answer to a question, or a classification of what appears in a photo. The NPU is the engine that runs these models within the device itself.

Instead of always relying on the cloud, many current phones can run a significant portion of these AI tasks locally, thanks to the NPU being much faster and more energy efficient than a classic CPU or GPU when it comes to these kinds of repetitive and massive calculations.

That's why, when your phone does things like blur the background in portrait mode, recognize your face to unlock it, or suggest quick replies in messages, there's usually an AI model behind it. running on the NPU and not on the CPUsaving time and battery.

CPU, GPU and NPU: the “complete team” of the SoC

To fully understand the role of the NPU, it's helpful to see how the work is divided with the CPU and GPU within the SoC, which is the true "brain" of the smartphone and integrates CPU, GPU, NPU, modem, image signal processor, video modules, and other blocks on a single tiny chip.

The CPU (Central Processing Unit) is the central processing unit. It is responsible for run the operating system, apps, and general logic of the device. It performs logical, arithmetic, and control operations, and coordinates what each component of the system should do at any given time.

The GPU (Graphics Processing Unit) is specialized in graphics. It is used for Rendering images, animations, 2D and 3D interfacesGames, visual effects, and anything that requires smooth pixel rendering. It excels at performing many graphics-related calculations in parallel.

The NPU comes into play when what is needed is not to draw graphics, but run AI models massively and quicklyAlthough some of these tasks could be done with the CPU or GPU, the NPU is designed from the ground up to do them better, faster, and using less energy.

In a football analogy, we could say that the CPU is the captain who organizes the play, the GPU is the striker in charge of the visual "fireworks," and The NPU is the coach that anticipates, analyzes patterns, and optimizes decisions. based on large amounts of data.

Why has the NPU become so important?

The emergence and widespread adoption of NPUs is not a marketing whim, but the answer to a very specific problem: The AI ​​tasks we use daily are becoming increasingly heavy and frequentAnd always running them in the cloud is not viable for either speed or privacy.

Things we now take for granted in a modern smartphone—portrait mode, advanced night mode, personalized recommendations, voice dictation, simultaneous translation, writing suggestionsVoice assistants that understand natural language, automatic video enhancements—are extremely computationally intensive.

Without an NPU, these tasks would have to be delegated to the CPU or GPU, which would mean a massive energy drain, an increase in the device's temperature and, in many cases, a constant need to connect to external servers in order to process everything.

The NPU allows much of this intelligence to run directly on the phone, which translates into faster speed (lower latency), longer battery life, and more privacyYour voice, photos, or videos can be processed locally without leaving the device in many functions.

Furthermore, since almost all recent chips include NPUs, operating system and app manufacturers have begun releasing AI-specific features that squeeze these units: Apple Intelligence, Galaxy AI, Xiaomi HyperAI, Google Pixel with Google TensorCopilot on mobile devices, etc.

How an NPU works on the inside

An NPU is composed of a large number of simple computing units that work in parallel. Its design is optimized for multiply and add matrices, apply convolutions, and perform repetitive operations which are typical of deep neural networks.

When an app wants to use AI on the device, it follows a fairly similar general workflow: first the data (an image, an audio, a text) they become a numerical representation that the NPU can handle, usually vectors or tensors.

Next, a pre-trained AI model—for example, an object or speech recognition model—is loaded into the NPU. This model consists of layers of artificial neurons connected to each other with weights. The NPU applies those weights and activation functions to the input data to produce an output.

The result can be a label (this photo contains a “dog”), an estimate (background-to-subject ratio in a scene), generated text, or a translation. The important thing is that The NPU can execute millions of these operations per second using less energy than the CPU or GPU.

Although NPUs are often compared to the human brain, there are important differences: The brain physically reorganizes its connections (neuroplasticity), while The NPU has a fixed architecture and the "connections" vary only at the software level., modifying weights in the models and not the hardware itself.

How does an NPU differ from a CPU and a GPU?

The CPU is optimized to perform a few very complex operations with high precision and flexibility. That's why it's perfect for General tasks, programmatic logic, thread management, system execution, and appsBut when you have to perform millions of simple operations at once, it stops being so efficient.

The GPU, being designed for graphics, already represented a leap forward in AI because it is capable of execute thousands of similar operations in parallelIn fact, Nvidia took advantage of this to apply its GPUs to machine learning tasks, long before commercial NPUs existed.

The NPU goes a step further: it sacrifices versatility in exchange for extreme optimization for the specific calculations that AI models need. It can deliver comparable or superior AI performance, using a fraction of the energy of a GPU or CPU for the same task.

On a mobile device, where battery life and heat dissipation are critical, this makes all the difference. A workload involving image or voice recognition that It would drain the battery quickly if it ran on CPU or GPU, it can be done continuously on the NPU with a much smaller impact.

This specialization also enables functions such as secure facial recognition, real-time camera analysis, or Contextual suggestions based on your device usage They can be active practically all day without you noticing serious drops in battery life.

Practical applications of the NPU in mobile

In everyday use, the NPU is involved in more aspects of your phone than you might think. One of the areas where it's most noticeable is the camera: portrait mode, night mode, noise reduction, and automatic scene identification. They use computer vision models that run on the NPU.

When you take a photo, the system can detect faces, recognize which parts of the image are sky, vegetation, or buildings, and adjust the parameters on the fly. It can also recognize people and group them in the gallery, or suggest the best shot among several captures thanks to AI.

Regarding voice, the NPU accelerates speech recognition for dictation, real-time transcription, and virtual assistants. This enables many of these functions to be to function without a permanent internet connectionreducing cloud dependence and improving privacy.

There are also many "invisible" features, such as analyzing your usage patterns to predict which apps you're going to open and preload themOptimize energy consumption, or show you more personalized recommendations. All of this is based on AI models that largely run on the NPU.

In addition, NPUs are used for security tasks: advanced facial unlocking, smarter fingerprint recognition, detection of suspicious access attempts, protection against abnormal behavior in apps and other functions that require analyzing large volumes of data quickly and locally.

Where else are NPUs used, besides in mobile devices?

Although we usually associate NPUs with smartphones, their use is becoming increasingly widespread. In data centers, large cloud providers use specialized accelerators for training and serving AI models on a massive scale, many of them with architectures similar to NPUs.

In the automotive world, autonomous vehicles and advanced driver assistance systems need to process Real-time information from cameras, radars, LiDAR and other sensorsThat's where chips with neural units capable of making instant decisions come into play.

NPUs or equivalent blocks are also being incorporated into edge devices such as smart security cameras, industrial equipment, IoT sensors and medical devices, which require local AI capabilities without relying on a permanent connection.

In the field of personal computing, some laptop and desktop processors from Intel, AMD, and Apple already include Dedicated AI blocks to accelerate vision, audio, video, and language modeling tasks, supporting the trend towards operating systems and desktop programs with integrated AI.

These environments share the same logic as mobile devices: running AI models on the device itself allows reduce latency, save bandwidth, and strengthen privacyas well as opening the door to new features that react instantly to what the user does.

Differences between NPU manufacturers and types

Although all NPUs pursue the same goal—accelerating AI calculations—they are not all the same. Each manufacturer designs its own architecture and decides... how many cores it dedicates, what type of operations it optimizes, and what memory bandwidth it uses It provides those nuclei.

Thus, Apple has its Neural Engine integrated into the A-series chips and the SoCs of its computers, Google is betting on its custom TPUs (Tensor Processing Units) in the Pixel and in its data centers, Qualcomm talks about its AI Engine in SnapdragonSamsung integrates NPUs into Exynos processors, and MediaTek does the same in the Dimensity family.

These differences not only affect raw power, but also energy consumption, compatibility with different development frameworks (TensorFlow, PyTorch, ONNX, etc.) and the ease for developers to leverage the NPU with their own apps and models.

In the world of data centers, companies like Nvidia, Intel, Google, and Graphcore offer specific accelerators with diverse architectureseach with different strengths depending on the type of model, the scale of deployment, or the cost and efficiency requirements.

For the end user, all this means that two phones that “have an NPU” can offer very different AI experiences: one can run more complex models locally, while the other It will depend more on the cloud or limit certain functions. to lighter models.

What are TOPS and how to measure the power of an NPU

When discussing the performance of an NPU, the figure of TOPS (Tera Operations Per Second) is often mentioned. trillions of operations per second). It is a way to measure how many mathematical operations the AI ​​processor can perform in one second.

The TOPS figure depends on factors such as the NPU's clock speed and the number of operational units it has. For example, a recent high-end chip might have around dozens of TOPS on your mobile NPU, while a desktop GPU dedicated to AI can easily exceed 1,000 TOPS.

However, TOPS are a guideline metric, because not all manufacturers calculate them the same way, nor do all AI tasks. They take advantage of that theoretical maximum figure.Furthermore, TOPS' actual requirements for a specific function are rarely explained to the user.

Even so, they serve to give a general idea: if you want to run more complex models (for example, large language models or image generators on the mobile phone itself), you are interested in the NPU having a high TOPS count and good energy efficiency.

There are specific benchmark applications like NPU Check, which run real language models locally to measure tokens per second, inference latency, and other parametersThese tools help you practically understand the limits of your device's NPU.

Privacy, operating systems, and is it possible to "escape" AI?

With the expansion of NPUs, a logical concern has arisen: if AI hardware already comes standard in almost all processors, Is it possible to avoid AI on a privacy-focused mobile device? or on an alternative operating system like GrapheneOS or Linux for smartphones?

The reality is that, for years now, virtually all SoCs used in mid-range and high-end devices include NPUs, or neural processing units. Even some budget phones are now using chips with dedicated AI capabilities, and New Linux phone projects are turning to commercial SoCs with integrated NPU.

At the hardware level, you can't "remove" that NPU from the chip. What you can do, and what privacy-oriented systems focus on, is control which software is allowed to use itwhat data it handles and whether or not it is allowed to send information to the cloud.

For example, you can disable voice assistants, camera features that recognize faces, or services that analyze usage patterns to generate recommendations. That doesn't eliminate the NPU, but it drastically reduces the extent to which it's used to analyze personal data.

In practice, it seems difficult to imagine a future without AI in devices, but it is realistic to aspire to scenarios where the user has greater control and transparency over which models are run, with what data, and for what purposesAnd that's where NPUs, by allowing more local processing, can be allies of privacy if used properly.

Which mobile phones have an NPU and since when?

The first commercial implementations of NPUs in smartphones date back to around 2017, when Huawei introduced the Kirin 970 with neural unit in the Mate 10And Apple launched its A11 Bionic chip with neural network processing capabilities in the iPhone 8 and later.

Since then, all the major manufacturers have incorporated neural units into their SoCs: Snapdragon, Exynos, Dimensity, Apple A-series, and Google's Tensor chips. They include blocks dedicated to AI one way or another.

For a while, you could still find very low-end phones without an NPU or with minimal AI capabilities, but in recent years the standard has become clear: Almost all modern smartphones include some form of NPUeven in lower price segments.

Specific examples range from iPhones with Neural Engine (used for Face ID, advanced computational photography, or voice analysis), to Samsung's Galaxy family, whose Exynos and Snapdragon processors integrate NPUs for Bixby, camera functions and Galaxy AI, going through the Google Pixel with the Tensor chip and its magic erase, live translation or advanced photo editing functions.

The current pace suggests that the NPU is already a standard component, just as the GPU or the 4G/5G modem were in their time. New system AI services—Apple Intelligence, Galaxy AI, Copilot, Gemini on Pixel, etc.— They explicitly rely on these neural processing units to be able to function without intolerable latency and without draining the battery.

What to look for when choosing a mobile phone with a good NPU

If you want a smartphone ready for the coming wave of AI (and the one that's already here), it's worth paying attention to several aspects related to the NPU and how the manufacturer harness that power for real-world applicationsnot only in technical specifications.

• NPU type and powerInvestigate which SoC the phone uses (Snapdragon, Exynos, Dimensity, Apple, Tensor, etc.) and what AI power it claims (TOPS). The more power and efficiency, the more headroom there will be for running complex models locally.
• Available AI featuresCheck out what the system offers: advanced computational photography, improved assistants, offline transcription, real-time translation, intelligent photo and video editing, personalized suggestions, etc.
• Software updatesAI evolves very quickly, so it's crucial that the manufacturer promises and delivers several years of updates. Many new AI features are coming. They arrive via software and make better use of the NPU over time.
• Privacy and controlCheck what options the system offers for managing AI permissions, which processes are done locally and which in the cloud, and how personal information is handled used by the models.
• Independent reviews and testsBeyond marketing, it's worth reviewing technical reviews and benchmarks (including those specific to NPUs) to see if the real-world AI performance lives up to expectations. It matches what was promised on paper..

By combining these factors, it is possible to find mobile phones where the NPU not only exists as a box to be checked, but actually offers superior camera experiences, more helpful assistants, faster translations and AI features that feel natural in everyday life.

In the end, the NPU has become a key piece of the puzzle: it's no longer something exotic reserved for premium models, but a core hardware component of almost any modern smartphoneUnderstanding what it does and how it affects the real experience helps us choose better and truly take advantage of the power of AI that we carry in our pockets.