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New notebooks gain battery life with this technology

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  Published in Science and Technology on Monday, September 15th 2025 at 7:25 GMT by PC World
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How Next‑Generation Laptops Are Turning Neural Processing Units Into Power‑Saving Powerhouses

The laptop market is witnessing a quiet revolution. While the public narrative around the next wave of portable computers has focused on slimmer bezels, higher‑resolution displays and longer battery lives, the real driver behind many of those gains is a hidden, specialized piece of silicon: the Neural Processing Unit (NPU). A recent PCWorld deep‑dive, “How Next‑Gen Laptops Use NPUs for Massive Power Savings,” traces how NPUs are becoming the unsung heroes of modern ultrabooks, and why that matters for both everyday users and tech enthusiasts.

The Problem with Traditional Power Demands

Until recently, laptops relied almost exclusively on general‑purpose CPUs and GPUs to handle everything from video decoding to machine‑learning inference. These components, while powerful, consume a lot of power and generate a lot of heat. The result? Heavy batteries, active cooling fans, and laptops that feel more like laptops than true “mobile” devices. The article opens with a quick comparison: a typical Windows laptop equipped with a mid‑range Intel Core i7 could see 10–15 W of power draw under light workloads, while heavy tasks—gaming, video editing, AI inference—could push that figure above 30 W.

Because CPUs and GPUs are designed to run a wide variety of workloads, they cannot be optimised for the very specific calculations that most machine‑learning (ML) tasks require. Even a “smart” CPU will execute a neural‑network inference by performing millions of generic multiply‑accumulate operations in a somewhat wasteful manner. The same is true of integrated GPUs. That inefficiency is what the article refers to as “silicon waste,” a problem that NPUs are built to eliminate.

What Exactly Is an NPU?

At its core, an NPU is a custom ASIC that executes tensor operations—matrix multiplications, convolutions, activations—in a highly parallel, low‑latency fashion. Unlike CPUs, NPUs can perform thousands of simple arithmetic operations simultaneously while staying in a low‑voltage domain that keeps power consumption to a minimum. They are the direct cousins of GPUs, but designed with a much narrower focus: ML inference and, increasingly, small‑scale training. The article cites several leading players in this space:

• Apple’s Neural Engine – Integrated into every Apple Silicon SoC (M1, M2, etc.), it is a dedicated 16‑core (and later 18‑core) NPU that can deliver 11 TOPS (trillion operations per second) on the M1 Pro, for example.
• Qualcomm’s AI Engine – Found in Snapdragon 8cx Gen 2 and later, this NPU can hit 6.5 TOPS while staying in a 2.5‑V domain.
• Intel’s Embedded AI Engine – The latest Lakefield SoC includes an integrated NPU that works alongside a 10‑core Xeon CPU, promising up to 4 TOPS.
• AMD’s upcoming RDNA‑3 “Compute‑Ready” GPU – AMD is hinting at an integrated AI engine in its next Ryzen mobile chips.

The article links to the Apple product pages, Qualcomm’s developer portal, and Intel’s Lakefield spec sheets, encouraging readers to dig into the numbers that illustrate the difference.

The Power‑Saving Equation

The key insight from the PCWorld article is that NPUs can reduce the CPU/GPU load by 50–70 % for inference‑heavy workloads. This is because the NPU handles all the heavy lifting of the neural network. When an app asks for a photo‑recognition or voice‑assistant request, the data is streamed to the NPU, which runs the inference in a fraction of the time and at a fraction of the power. The CPU can then sleep or run background tasks at a lower clock speed.

A concrete example the article cites is the Apple MacBook Air with the M1 chip. Apple claims a 12‑hour battery life for web browsing and a 15‑hour battery life for media playback. Under heavy ML usage (e.g., using the Vision framework for real‑time object detection), the NPU keeps power consumption under 10 W, compared to a CPU‑only design that would draw 20–25 W. This means not only longer battery life but also less heat and quieter fans—sometimes eliminating the fan altogether.

The article also notes that NPUs support dynamic voltage and frequency scaling (DVFS) tailored to the workload. When a simple inference is running, the NPU can drop its clock speed, reducing power consumption further. This fine‑grained control is a feature not easily achievable in GPUs, which are built to maintain a steady performance envelope.

Real‑World Use Cases

The piece doesn’t limit NPUs to “smartphone‑style” tasks; it showcases real applications in laptops:

1. Photography & Video – Apple’s Photos app can use the Neural Engine for intelligent image enhancement, while Android laptops with Snapdragon 8cx can accelerate HDR video processing.
2. Speech Recognition – Windows 11’s voice typing now uses the embedded AI engine to deliver faster and more accurate transcription.
3. Augmented Reality – The Dell XPS 13 2‑in‑1’s new “XPS Vision” module uses an NPU‑based inference pipeline to process depth‑maps in real time, enabling immersive AR overlays without a dedicated GPU.
4. Gaming – While NPUs are not meant to replace GPUs in high‑end gaming, they can still offload physics‑based AI (like NPC behavior) to save power, which is critical in fanless designs.

In each case, the article points readers to a demo or a tech‑review video that demonstrates the speedup and battery improvement. For example, a side‑by‑side benchmark shows the Snapdragon 8cx Gen 2 completing a 50‑layer ResNet‑50 inference in 0.5 seconds while drawing 1.2 W, versus 3.0 seconds and 3.8 W on a comparable Intel i7 mobile CPU.

The Trade‑Offs and Future Outlook

Like any technology, NPUs come with limitations. Their specialized architecture means they are great at specific tensor ops but not suitable for general computation. Software support is still maturing: developers must use frameworks like TensorFlow Lite, Core ML, or ONNX Runtime to target NPUs. That said, most major operating systems now expose a uniform “AI acceleration” API that hides the underlying hardware complexity.

The article’s closing section looks forward to the next wave: NPUs with on‑chip memory (HBM) for larger models, better interconnects for multi‑chip systems, and edge‑AI standards that make it easier to port models across vendors. It also touches on 5G‑edge convergence: as network latency drops, laptops can offload heavy ML tasks to the cloud, but local NPUs will still be necessary for privacy‑sensitive or latency‑critical applications.

Bottom Line

The PCWorld piece is a compelling reminder that the future of mobile computing isn’t just about thinner displays or faster CPUs. It’s about intelligently combining the right silicon for the right job. NPUs, with their highly parallel, low‑power design, are enabling laptops that run smarter, quieter, and longer on a single charge. As more manufacturers embed NPUs in their next‑gen chips—whether Apple’s M1, Qualcomm’s Snapdragon 8cx, Intel’s Lakefield, or AMD’s upcoming Ryzen—users can expect laptops that feel less like desktop clones and more like true mobile devices, powered by a new generation of silicon that makes efficiency as important as performance.