Neural processing units (NPUs), specialized chips designed to run artificial intelligence tasks, are moving beyond smartphones into smartwatches and wireless earbuds for the first time, fundamentally changing what these devices can do without relying on your phone. Flagship smartphone NPUs from Qualcomm, MediaTek, Samsung, and Apple now deliver up to 100 trillion operations per second (TOPS) of computing power, and vendors are scaling that performance by roughly 30 percent annually . The shift signals a major transition in consumer electronics, where on-device AI is becoming a genuine competitive advantage rather than a marketing gimmick.

What's Driving the NPU Explosion in Wearables?

The push toward dedicated neural processors in wearables stems from real consumer demands. Smartphones are using NPUs to power on-device generative AI, computational photography, real-time translation, and large language model support . These capabilities require significant computing power, but running them locally on the device, rather than sending data to a cloud server, offers three critical advantages: speed, privacy, and cost. As these use cases mature, manufacturers are now embedding similar capabilities into smaller devices.

The wearable market is experiencing rapid transformation. According to market research firm Futuresource Consulting, 94 million smartwatches were sold globally in 2025 . Apple was among the first to embed AI capability through its Apple Neural Engine (ANE), but dedicated NPUs are now appearing more widely across the smartwatch category. Qualcomm's newly revealed Snapdragon Wear Elite, claimed to be the first smartwatch chip with a general-purpose NPU, establishes a baseline of 12 TOPS for the category, signaling how quickly the competitive landscape is shifting .

True wireless earbuds represent an even larger opportunity. Over 360 million TWS (true wireless stereo) headphones ship annually, and because each earbud typically contains its own system-on-chip (SoC), the semiconductor opportunity exceeds 700 million chip units per year . Five vendors currently control 92 percent of that market. AI is being embedded across this landscape through proprietary NPUs and digital signal processor (DSP) based approaches, with the shift toward genuinely untethered, AI-capable hearable products accelerating.

How Are Operating Systems Preparing for NPU-Powered Devices?

Behind the scenes, operating system developers are laying the groundwork for widespread NPU adoption. Linux 7.1, the latest kernel release, includes several critical updates for neural processor support. The kernel now includes per-process memory usage queries for the AMDXNA driver used by Ryzen AI NPUs, and power estimate reporting for Ryzen AI NPUs . More importantly, the kernel includes a safeguard for Intel NPUs to prevent a single program from exhausting all NPU resources, a practical necessity as more applications compete for neural processing power .

These kernel-level improvements reflect a broader industry recognition that NPU management requires careful resource allocation. As more devices gain neural processors, operating systems must ensure fair access and prevent performance bottlenecks.

Steps to Understanding NPU Performance Metrics

• TOPS (Trillion Operations Per Second): The headline metric used to compare NPU performance, but it often obscures important details about what types of operations the chip excels at and real-world performance in specific applications.
• Architecture Optimization: Different NPU designs are optimized for different tasks; some excel at image processing, others at language models, so comparing raw TOPS numbers between vendors can be misleading without understanding architectural differences.
• Power Efficiency: Wearable devices have strict power budgets, making power-per-operation a critical metric that matters more than peak performance in many consumer applications.

What Does the NPU Roadmap Look Like Through 2030?

Futuresource Consulting's analysis of edge AI silicon trajectories provides concrete forecasts for the next four years. The research firm expects smartphone NPU performance to almost triple by 2030, while smartwatches should at least double their neural processing capabilities . True wireless headphones could soon be on a trajectory toward on-device processing capabilities that make the smartphone an optional companion rather than a required one.

"These are not speculative scenarios. They are the logical product of chip design trends already in motion. Edge AI offers real advantages in speed, privacy and cost, and traditional coded algorithms are being replaced by machine learned versions that increase efficiency while expanding capabilities. For CE brands, understanding where AI compute is heading, and what the silicon enables, is becoming a fundamental strategic necessity," stated Simon Forrest, Head of Core Technology at Futuresource Consulting.

Simon Forrest, Head of Core Technology, Futuresource Consulting

The implications are profound. As NPU performance scales across wearables, the devices themselves become smarter and more independent. A smartwatch with a capable neural processor could handle real-time health monitoring, voice commands, and even local language processing without constantly communicating with a smartphone. Wireless earbuds could offer on-device noise cancellation, real-time translation, and personalized audio processing without cloud connectivity.

The silicon decisions being made today will define the product landscape through 2030 . Manufacturers choosing which NPU architecture to embed in their devices are essentially betting on which AI capabilities will matter most to consumers over the next four years. Some vendors are investing in proprietary NPU designs optimized for specific tasks, while others are adopting general-purpose neural processors that offer flexibility at the cost of peak performance in any single domain.

This transition represents a fundamental shift in how consumer electronics work. For decades, wearables have been tethered to smartphones for computational heavy lifting. As NPUs become standard components in smartwatches and earbuds, that dependency weakens. The devices themselves become capable of running meaningful AI workloads locally, improving responsiveness, protecting user privacy, and reducing reliance on cloud services. By 2030, the wearable devices in your pocket may be far more computationally capable than the smartphones of just a few years ago.