Neural processing units (NPUs) are moving beyond laptops and smartphones into the microcontrollers that power everyday devices like wearables, home electronics, and factory equipment. Texas Instruments has launched two new microcontroller families with built-in NPUs, signaling a broader shift toward bringing artificial intelligence capabilities to the edge of the network, where data is generated and used, rather than sending everything to distant cloud servers .

What Are Neural Processing Units and Why Do They Matter?

Neural processing units are specialized processors designed specifically to accelerate machine learning workloads, particularly the mathematical operations that power neural networks. Unlike central processing units (CPUs), which handle general-purpose computing tasks, or graphics processing units (GPUs), which juggle graphics and parallel computation, NPUs are tuned exclusively for AI inference, the process of running trained AI models to make predictions or decisions .

The key advantage is efficiency. By offloading AI tasks to a dedicated processor, devices can run sophisticated AI features while consuming far less power than traditional approaches. This matters enormously for battery-powered devices like smartwatches, hearing aids, and wireless sensors that need to operate for days or weeks on a single charge .

How Are Companies Bringing AI to Tiny Devices?

Texas Instruments' new offerings represent a significant step in democratizing edge AI. The MSPM0G5187, built on an Arm Cortex-M0+ processor core, targets cost-sensitive applications and is priced below $1 in volume production, making it accessible for manufacturers adding AI features to simple, inexpensive devices . The chip integrates TI's TinyEngine NPU, allowing AI workloads to run alongside the main CPU without requiring additional components.

For industrial applications, the AM13Ex series combines an Arm Cortex-M33 processor core, real-time control functions, and the NPU in a single chip. This allows designers to manage motor control and AI tasks simultaneously without extra hardware, reducing both system cost and complexity .

Steps to Evaluate NPU Performance for Your Application

• Measure TOPS Requirements: Determine how many trillions of operations per second your AI workload needs. Current consumer chips range from roughly 10 to 45+ TOPS for on-device AI, with lower-cost microcontrollers offering less .
• Test Real-World Latency: Run actual AI tasks like noise suppression, background blur, or local transcription to measure response time and power consumption under realistic conditions, not just synthetic benchmarks .
• Assess Memory and Model Size: Verify that your trained AI model can fit within the device's available memory after compression techniques like quantization and pruning are applied .
• Evaluate Development Support: Check whether the chipmaker provides software tools, pre-built models, and documentation to accelerate your time to market .

What Real-World Applications Are Emerging?

The practical applications are already taking shape. Wearables can now perform real-time transcription, background noise suppression, and eye-contact correction without draining batteries or uploading audio to the cloud. Industrial equipment can run predictive maintenance models locally, detecting equipment failures before they happen. Home electronics can process voice commands and perform image recognition without sending data to external servers .

Texas Instruments is supporting both microcontroller families with an AI-focused software environment that helps engineers train and deploy models more easily. The platform includes dozens of pre-built models to accelerate development, allowing teams to skip months of model training and optimization .

Why Is This Shift Happening Now?

The move reflects a fundamental change in how the industry thinks about AI deployment. Rather than treating the personal computer or smartphone as a portal to cloud-based AI services, manufacturers are recognizing that keeping data and processing local offers significant advantages: lower latency, reduced bandwidth costs, improved privacy, and better battery life .

This trend extends beyond consumer devices. The AI PC and ARM laptop wave demonstrates that even high-performance computing is shifting toward on-device processing. Operating systems like Windows, macOS, and Linux are being redesigned to assume the presence of AI accelerators, with features like AI-powered search, live captions, and context-aware assistants increasingly requiring a minimum NPU specification .

What Challenges Remain for Developers?

Despite the promise, several hurdles persist. Model compression, the process of shrinking AI models to fit on resource-constrained devices, remains complex and often requires specialized expertise. Developers must balance model accuracy against size and power consumption, a trade-off that varies by application. Additionally, the fragmentation of NPU architectures across different chipmakers means that models optimized for one device may not run efficiently on another .

The MSPM0G5187 is already available in production volumes, while the AM13Ex series is currently in preproduction, with more versions expected by the end of 2026 . This timeline suggests that manufacturers will have access to a growing range of NPU-equipped microcontrollers over the coming months, accelerating adoption across consumer and industrial markets.

The convergence of cheaper NPUs, better development tools, and pre-built AI models is creating a new baseline for what embedded devices can do. Within the next few years, on-device AI will likely become as standard in microcontrollers as Wi-Fi connectivity is today, fundamentally changing how billions of everyday devices operate.