Understanding Neural Processing Units (NPUs): The Future of AI Hardware

If you’ve ever used a smartphone that recognizes your voice or a camera that suggests better shots, you’ve seen an NPU in action. Neural Processing Units (NPUs) are one idea that seems obvious in hindsight: build chips specifically for AI tasks instead of bending general-purpose hardware, which often leads to inefficiencies like higher power consumption and slower processing speeds, to do the job. But until recently, we didn’t need them. CPUs, and later GPUs, were enough. Now they’re not.

Why NPUs?

AI workloads have grown too big. Training a large machine learning model, or even running it, takes a staggering number of calculations—up to 10²⁶ (or 100,000,000,000,000,000,000,000,000) floating point operations (FLOP) for some of the most advanced models as of December 2024. To put this into perspective, performing 10²⁶ FLOP is roughly equivalent to the total number of calculations humanity has done manually throughout its history. To put it another way, the human brain is estimated to perform approximately 10¹⁵ to 10¹⁷ FLOPs per second in biological neural computation, making 10²⁶ FLOPs similar to several millions of years of brain activity compressed into one training process.

This exponential growth reflects significant investments in AI infrastructure and advancements in hardware capabilities.

To understand why NPUs are so effective, it helps to look at what they don’t do. CPUs spend a lot of effort juggling tasks—fetching data, running logic, managing memory—and this general-purpose design makes them less efficient for specialized AI tasks. NPUs, on the other hand, are streamlined to focus entirely on neural network computations, performing them with far greater speed and consuming significantly less power. GPUs handle parallel processing, which is why they’ve been useful for AI. However, both CPUs and GPUs still have fundamentally general purposes. They’re not designed for AI; they’re adapted for it.

NPUs, by contrast, are designed from the ground up to accelerate AI workloads. They use architectures optimized for the linear algebra operations that neural networks rely on. They’re also better at handling low-precision calculations, common in AI but less so elsewhere.

This isn’t just a theoretical advantage. It translates into real-world improvements. A smartphone with an NPU can run complex AI models locally, instead of sending data to the cloud and waiting for a response. This means faster results and better privacy. In a data center, NPUs can handle AI workloads more efficiently, reducing energy costs and freeing up other hardware for different tasks.

The Rise of Edge AI

One of the most exciting applications for NPUs is at the edge. Edge computing means processing data locally, near the device that generates it, instead of sending everything to the cloud. For instance, autonomous vehicles use NPUs to process sensor data, such as identifying objects on the road and predicting their movement, all in real time. Similarly, smart home devices, like advanced thermostats, use NPUs to analyze environmental data and adjust settings dynamically without relying on cloud connectivity. This is crucial for applications like autonomous vehicles, where split-second decisions can’t wait for a round trip to the cloud.

NPUs make edge AI possible. Without them, edge devices would be too slow or power-hungry to run the necessary models. With NPUs, they can. This is why you’re starting to see NPUs in smartphones, cameras, and even home appliances. They’re turning everyday devices into smart devices.

Industries Driving NPU Adoption

NPUs aren’t just a tool for AI researchers. They’re becoming a foundational technology across industries.

• Consumer Electronics: The most obvious use case is in smartphones. Apple’s Neural Engine and Google’s Tensor Processing Unit (essentially an NPU designed for large-scale data center workloads) are examples of NPUs designed for consumer devices. They handle tasks like image recognition, voice commands, and augmented reality.
• Healthcare: NPUs are enabling new applications in medical imaging and diagnostics. For example, a portable ultrasound machine with an NPU can analyze images in real time, helping doctors make faster decisions.
• Autonomous Vehicles: Self-driving cars rely heavily on edge AI. NPUs process sensor data to identify objects, track movement, and make driving decisions, all in real time.
• Data Centers: Cloud providers are using NPUs to make AI services faster and more cost-effective..

Challenges Ahead

NPUs face challenges despite all their advantages. One is competition. As AI becomes more central to computing, other specialized chips are emerging. TPUs, designed by Google, are one example. FPGAs (Field-Programmable Gate Arrays) are another. Each has its niche.

Another challenge is software. Hardware is only as good as the software that runs on it. Developers need tools to build and optimize models for NPUs. Thanks to mature ecosystems like CUDA, GPUs still have an edge in this area.

Finally, there’s the issue of adoption. NPUs are a new technology, and industries take time to adopt new technologies. Barriers include the high initial cost of implementing NPU-based systems and the lack of a mature ecosystem to support them. Developers often face challenges in integrating NPUs into existing workflows, as software tools and frameworks are still evolving to keep pace with hardware advancements. 

The Future of AI Hardware

It’s tempting to think of NPUs as replacing CPUs and GPUs. But the reality is more complex. NPUs will coexist with other types of chips, each serving different roles. CPUs will still handle general-purpose tasks. GPUs will still be important for training. But for inference, especially at the edge, NPUs will likely become the default.

This shift is already happening. It’s hard to imagine a smartphone without an NPU in five years. The same might be true for cars, cameras, and countless other devices in ten years.

Partner with Linkt.ai for AI Excellence

At linkt.ai, we specialize in training and fine-tuning models tailored to your business needs. Our expertise ensures you don’t have to navigate the complexities of AI hardware, such as NPUs or CPUs, on your own. Let us handle the technical challenges so you can focus on delivering value to your customers.