A neural engine is a specialized chip (or part of a chip) built specifically to handle artificial intelligence tasks like facial recognition, photo enhancement, and voice processing. Unlike the general-purpose processors in your phone or laptop, a neural engine is designed from the ground up to run the math that powers machine learning, and it does so using a fraction of the energy a standard processor would need. Apple popularized the term when it debuted the first Neural Engine inside the A11 Bionic chip in September 2017, launching alongside the iPhone 8 and iPhone X.
How It Differs From a CPU or GPU
Your device already has a CPU (the main brain that handles everyday tasks) and often a GPU (a graphics processor that handles visual workloads). Both can technically run AI calculations, but neither was designed for that purpose. A CPU processes instructions one after another, making it slow at the massively parallel math AI requires. A GPU handles parallel work much better, thanks to its many processing cores, but it consumes a lot of power doing so.
A neural engine, sometimes called a neural processing unit (NPU), takes a different approach. It’s purpose-built hardware that strips away features a GPU would need for general graphics work and instead optimizes entirely for AI. It includes dedicated circuits for the specific multiplication and addition operations that neural networks rely on, plus high-speed memory built right into the chip so data doesn’t have to travel far. The result is a processor that matches or exceeds a GPU’s parallel computing ability for AI tasks while drawing significantly less power.
The Math Behind It
Nearly every AI model, whether it recognizes your face or sharpens a photo, boils down to enormous amounts of matrix multiplication. Think of it as taking huge grids of numbers and multiplying them together, over and over, millions of times per second. The core operation is called a multiply-accumulate (MAC): multiply two numbers together, then add the result to a running total. A neural engine packs in thousands of dedicated MAC units that can execute these operations simultaneously, which is why it processes AI workloads so much faster than a general-purpose chip working through the same calculations.
Why It Uses Less Power
Traditional chip designs separate the processing unit from memory. Every time the processor needs a piece of data, it has to fetch it from a separate memory bank, and that constant back-and-forth burns energy. Neural engines solve this by placing high-speed memory as close to the processing circuits as possible, sometimes even performing calculations directly inside the memory itself. This “near-memory” or “in-memory” computing approach dramatically cuts the energy cost of shuttling data around.
That efficiency matters most in battery-powered devices. Running a complex AI model on a phone’s GPU would drain the battery quickly. A neural engine can handle the same task while sipping power, which is why modern smartphones can run dozens of AI features throughout the day without a noticeable hit to battery life.
What It Actually Does on Your Device
If you’ve used Face ID, portrait mode, or voice dictation on a modern phone, you’ve used a neural engine. Apple began applying deep learning to face detection starting with iOS 10, and the Photos app uses multiple machine learning models running privately on-device to recognize people, organize images, and enhance Live Photos. These tasks run on the neural engine rather than the main processor.
On Windows PCs with NPUs, features like background blur in video calls, automatic eye contact correction, and noise suppression for microphones run through the neural engine. Microsoft Teams, for example, uses the NPU to handle background segmentation for blur and virtual background effects, keeping those tasks off the CPU so the rest of the system stays responsive.
Beyond those visible features, neural engines power real-time translation, predictive text, computational photography (like adjusting lighting and depth in photos after you take them), and augmented reality experiences that need to track objects and surfaces in real time.
Privacy Benefits of On-Device AI
One of the most significant advantages of having a neural engine in your device is that AI processing can happen locally, without sending your data to a cloud server. When your phone recognizes faces in your photo library, that analysis stays entirely on the device. Your financial records, health data, voice recordings, and personal images never leave your phone or laptop.
This matters for everyday privacy, but it’s especially important in industries like healthcare, finance, and law, where transmitting sensitive data to external servers could violate compliance rules. On-device processing also reduces the number of potential attack points. There are no API calls to intercept, no data transfers across networks to exploit. The information simply never leaves the endpoint.
How Developers Use It
Software developers don’t program a neural engine directly. Instead, they use frameworks that automatically route AI workloads to the right hardware. On Apple devices, the CoreML framework lets developers build machine learning features into apps, and the system decides whether to run those models on the neural engine, GPU, or CPU depending on what’s available. On Windows, developers can tap into the NPU through standardized APIs. Apps can discover whether neural engine effects are supported, turn them on or off, and access metadata about the hardware’s capabilities.
This abstraction layer means app developers don’t need to understand the chip’s architecture. They build the AI model, hand it to the framework, and the operating system handles the rest. As neural engines become standard across phones, laptops, and tablets, more apps are offloading AI tasks to them automatically, without the user needing to configure anything.
Where Neural Engines Are Headed
Every major chip manufacturer now builds neural engines into their processors. Apple’s latest chips contain neural engines capable of trillions of operations per second, a massive leap from the original A11’s 600 billion. Qualcomm, Intel, AMD, Samsung, and Google all include NPU hardware in their latest chips. The term “AI PC” that you see in marketing today essentially means a computer with a built-in neural engine powerful enough to run large AI models locally, from generating text to processing video in real time, without relying on cloud servers or draining your battery.