Neural processing units (NPUs) are no longer optional extras on consumer devices; they're becoming the defining feature of modern computing hardware. The global System-on-Chip (SoC) market, which integrates CPUs, GPUs, NPUs, and other processors onto a single semiconductor platform, reached $210.3 billion in 2026 and is projected to expand to $413.4 billion by 2035, according to market research from Globe Market Research. This 7.8% annual growth rate reflects a fundamental shift in how device makers are designing chips.

What's driving this explosive growth? The answer lies in a simple reality: computing is no longer one-size-fits-all. A central processing unit (CPU) excels at unpredictable, branchy work like running your operating system or handling database queries. A graphics processing unit (GPU) dominates at parallel tasks like rendering images or training massive AI models. But neither is ideal for the AI workloads happening right now, on your phone, in your car, or on your laptop.

What Exactly Is an NPU, and Why Does Your Device Need One?

An NPU, or neural processing unit, is a specialized chip designed to run artificial intelligence models locally on your device, without sending data to the cloud. Think of it as a dedicated brain for AI tasks like face recognition, voice transcription, photo enhancement, and background blurring on video calls. Apple calls theirs the Neural Engine; Qualcomm and Intel have their own versions. The key difference from data center AI accelerators like Google's TPU (tensor processing unit) is the operating environment: NPUs are optimized for battery-powered devices running small models continuously, while TPUs are plugged into walls and trained on enormous models.

The practical benefit is significant. Running AI locally means no round trip to a cloud server, no battery drain from constant wireless transmission, and no need to upload sensitive data like your face or voice to someone else's server. It's faster, more private, and more efficient. That's why every "AI PC" sticker on a laptop is really advertising an NPU.

How Are Chip Makers Integrating NPUs Into Modern Devices?

The latest generation of SoCs demonstrates how aggressively the industry is moving toward AI-centric design. Rather than treating the NPU as an afterthought, manufacturers are now building entire chip architectures around heterogeneous computing, meaning multiple specialized processors working together.

• Qualcomm's Snapdragon 8 Elite Gen 5: Combines CPU, GPU, NPU, connectivity, and sensing functions on a single chip. The Hexagon NPU is 37% faster than its predecessor and delivers 16% better AI performance per watt, expanding AI processing directly inside smartphones and mobile devices.
• MediaTek's Dimensity 9500: Incorporates the NPU 990 for generative and agentic AI, delivering more than 2x faster token generation compared with previous generations. This increased speed is driving demand for AI-optimized SoCs across the industry.
• Samsung's Exynos 2600: Built on an industry-first 2-nanometer process, this chip integrates CPU, GPU, and NPU within one mobile platform and provides 113% higher generative AI performance than its predecessor.
• Intel Core Ultra Series 3: Integrates CPU, GPU, and NPU resources into a single platform with up to 180 aggregate TOPS (tera operations per second), with top models providing up to 50 NPU TOPS. Intel specifically highlights lower system complexity and improved total cost of ownership compared with separate CPU and GPU configurations.
• AMD Ryzen AI Max PRO 400: Combines CPU, integrated graphics, NPU, and up to 192 gigabytes of unified system memory, supporting complex local generative and agentic AI workloads that previously depended more heavily on cloud infrastructure.

These aren't incremental improvements. They represent a wholesale rearchitecture of consumer computing hardware around the assumption that AI processing is now a core workload, not a luxury feature.

Why Is Asia Pacific Dominating the SoC Market?

Asia Pacific captured 50.6% of the global SoC market in 2026, representing approximately $106.41 billion. This dominance reflects the region's massive semiconductor manufacturing base, extensive electronics production ecosystem, strong consumer device demand, and concentration of major chip fabrication and assembly activities. The region's leadership is expected to continue as demand for AI-optimized chips grows.

Portable electronic devices, including smartphones, tablets, laptops, and wearables, account for 37.8% of the SoC market by application. Consumer electronics more broadly, including smart TVs, gaming devices, and connected home products, capture 42.3% of the market. These categories are driving SoC adoption because modern devices need more computing functionality within tighter power, thermal, cost, and physical space constraints.

What's Changing in How Chips Are Designed?

The shift toward AI-centric SoCs reflects a deeper truth about modern computing: the workload has changed shape. Smartphones, vehicles, PCs, and connected devices no longer need just a fast general-purpose processor. They need a heterogeneous architecture that can handle multiple types of computation efficiently. Rather than assembling separate processors for every workload, SoCs now combine general-purpose processing, AI acceleration, graphics, security, networking, and multimedia functions into a tightly integrated design.

A principal consultant at Globe Market Research explained the market dynamics: "System-on-Chip solutions are gaining adoption as device makers seek higher performance, lower power consumption, compact designs, and greater functional integration across smartphones, automotive systems, IoT devices, wearables, and edge computing. Buyers are prioritizing processing efficiency, connectivity, security, and scalable architectures, while suppliers with advanced design capabilities and strong semiconductor ecosystems are positioned for wider adoption".

This architectural shift is being enabled by advances in semiconductor manufacturing. TSMC's N2 process entered volume production in the fourth quarter of 2025, with N2P and A16 scheduled for production in the second half of 2026. These advanced process nodes improve SoC performance, power efficiency, and transistor density, making it possible to pack more functionality into smaller, cooler chips.

How Does This Affect Automotive and Enterprise Computing?

The NPU trend extends beyond consumer devices. Automotive SoCs are becoming AI domain controllers, consolidating multiple vehicle functions into centralized processors. NXP's S32N7, for example, consolidates up to eight vehicle domains within one automotive processor architecture, accelerating the shift toward software-defined vehicles.

In enterprise computing, the integration of NPUs into platforms like AMD Ryzen AI Max PRO 400 is enabling complex local AI workloads that previously required cloud infrastructure. With up to 192 gigabytes of unified system memory, these chips can run large language models and agentic AI systems locally, reducing latency and eliminating cloud service costs.

What Does This Mean for the Future of Computing?

The convergence of CPU, GPU, and NPU capabilities into single SoCs represents a fundamental rethinking of how computing hardware should be organized. Rather than treating AI as a specialized workload that requires separate infrastructure, chip makers are embedding it as a core function alongside traditional computing tasks. This trend is expected to accelerate as agentic AI, which can learn and adapt locally on devices, becomes more common.

The market projections are clear: by 2035, the SoC market will nearly double in size, driven primarily by the integration of AI acceleration into mainstream devices. For consumers, this means faster, more private, and more efficient AI features built directly into the hardware you use every day. For device makers, it means rethinking chip design from the ground up to balance performance, power efficiency, and the growing computational demands of on-device AI.