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What’s NPU, and why is everyone suddenly interested in it?

We’re in the midst of the AI era, and as generative AI makes strides, big players like Intel, AMD, and Qualcomm are diving into the hardware discussion. The NPU (neural processing unit) is being introduced to accelerate AI processes, at least in theory. Apple has had NPUs in their chips for quite some time, so they’re not exactly a novelty. However, they’re now being touted as the “next big thing” in various industries and are more crucial than ever.

In simple terms, an NPU is a special processor made specifically to run machine learning algorithms. Unlike regular CPUs and GPUs, NPUs are fine-tuned to handle the intricate math involved in artificial neural networks.

They’re really good at handling loads of data at the same time, which makes them perfect for jobs like recognizing images, processing natural language, and other AI tasks. To break it down, if you throw an NPU into a GPU setup, the NPU could take on a particular job like spotting objects or speeding up image processing.

The NPU is crafted with a design featuring a “data-driven parallel computing” architecture. This setup is excellent at handling enormous multimedia data, like videos and images. It basically mimics how human neurons and synapses work at the circuit level, directly processing them using a deep-learning instruction set. This clever approach lets the NPU handle a bunch of neurons with just one instruction, making it super efficient for AI tasks.

Sure, the main processor could handle these tasks, but it would gobble up power and hog resources from other apps. On the flip side, the NPU smoothly tackles these complex calculations using super-efficient circuitry and its own dedicated memory. It cranks out lightning-fast results for AI apps, leaving the main chip to chug along without a hitch.

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How is NPU different from CPU and GPU?

So, GPUs (graphics processing units) are good at juggling multiple tasks simultaneously and are commonly used in machine learning. Now, NPUs take specialization up a notch. GPUs are all-around champs, particularly in graphic rendering and parallel tasks. On the flip side, CPUs (Central Processing Units) are like the general brains of a computer, tackling a wide variety of tasks.

But NPUs are like the superheroes custom-made to speed up deep learning algorithms. They’re designed to nail down the exact operations needed for neural networks. This high level of specialization means NPUs can kick it up a notch and outperform CPUs and even GPUs in certain situations when it comes to handling AI tasks.

There’s this cool idea floating around called GPNPU (a mix of GPU and NPU), trying to blend the best of both worlds. GPNPUs use the powerhouse parallel processing skills of GPUs and throw in NPU architecture to turbocharge AI tasks. The goal here is to find that sweet spot between being versatile and nailing specialized AI processing, all packed into one chip to handle a variety of computing needs.

NPUs are important for machine learning algorithms

Machine learning algorithms are like the backbone of AI applications. People sometimes mix them up, but think of machine learning as a specific kind of AI. These algorithms get smart by learning from data patterns, making predictions and decisions without someone explicitly telling them what to do. And hey, there are four flavors of machine learning algorithms: supervised, semi-supervised, unsupervised, and reinforcement.

NPUs are the key players in making these algorithms run smoothly. They handle important jobs like training and inference, crunching through massive datasets to fine-tune models and spit out real-time predictions.

What lies in the future for NPUs?

NPUs are making waves in 2024, especially with Intel’s Meteor Lake chips taking the spotlight. Whether they’ll be a game-changer in the future is still up in the air. The idea is that beefed-up AI abilities could bring about more advanced applications and better automation, potentially making it easier to use across different fields.

After that, we can expect a spike in the craving for AI-powered apps, and NPUs will be leading the charge. Thanks to their special setup tailored for machine learning tasks, NPUs are pushing the boundaries in the computing realm. The combo of GPNPUs and the improvements in machine learning algorithms is bound to spark advancements we haven’t witnessed yet, propelling technology forward and reshaping our digital world.

At the moment, NPUs might not be a big deal for most folks, just making stuff like background blur on Zoom or local AI image creation faster on your PC. But down the road, as AI features become a norm in more applications, NPUs could very well become a must-have part of your PC.

Are NPUs available in smartphones?

After hitting the scene a few years back, NPU technology has quickly stepped up its game and is now a standard feature in smartphones from almost every major brand.

Apple took the lead by bringing in the Neural Engine NPU in its A11 mobile chipset and top-tier iPhone models in 2017. Following suit, Huawei introduced its NPU with the Kirin 970 system on a chip in 2018. Qualcomm, the big player in Android mobile platforms, also jumped on the bandwagon, integrating its AI Engine into its high-end 800 series chipsets.

Lately, Qualcomm has set its sights on on-device generative AI, rocking the NPU on the Snapdragon 8 Gen 3. And guess what? MediaTek and Samsung are doing the same, embedding NPUs into their latest gadgets.

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Why is specialized AI hardware required?

GPUs hit a wall in AI performance. As neural networks and other machine learning models got bigger and trickier, GPUs couldn’t quite keep pace. While they did fine with parallel math calculations, they weren’t exclusively crafted with AI in focus.

As networks got bigger, relying solely on GPU parallelism started dragging down performance. Toss in the power limits of smartphones, and it became obvious that we needed specialized hardware.

We needed dedicated AI hardware to move beyond just relying on GPUs. Enter the Neural Processing Unit or NPU – a microprocessor tailor-made for handling neural network machine learning. Simply put, an NPU’s expertise lies in crunching numbers for machine learning models.

Vishal Kawadkar
About author

With over 8 years of experience in tech journalism, Vishal is someone with an innate passion for exploring and delivering fresh takes. Embracing curiosity and innovation, he strives to provide an informed and unique outlook on the ever-evolving world of technology.