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Texas Instruments unveils AI-powered microcontrollers for edge computing breakthroughs

From health monitors to humanoid robots, TI's latest chips redefine edge AI. Discover how TinyEngine cuts latency and energy use—without sacrificing performance.

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2 min read
The image shows an open book with a variety of machines and text on it. The book is filled with...
The image shows an open book with a variety of machines and text on it. The book is filled with pictures of various machines, each with its own unique design and purpose. The text on the book provides further information about the machines and their functions.

Texas Instruments unveils AI-powered microcontrollers for edge computing breakthroughs

Texas Instruments (TI) has launched two new microcontroller (MCU) families designed for edge artificial intelligence (AI). The MSPM0G5187 and AM13Ex MCUs include a dedicated neural processing unit (NPU) called TinyEngine. This hardware accelerator aims to improve efficiency in AI tasks while reducing energy use and latency.

The new chips target a wide range of applications, from wearable health devices to industrial automation and humanoid robots. TI's move signals a push to make edge AI more accessible across different electronic systems.

The MSPM0G5187 MCU features an Arm Cortex-M0+ core paired with TinyEngine, which cuts down on flash memory requirements and speeds up AI processing. Compared to similar MCUs without an accelerator, it delivers lower latency and better energy efficiency. This makes it suitable for battery-powered devices and real-time monitoring applications.

Meanwhile, the AM13Ex MCUs combine an Arm Cortex-M33 core, TinyEngine, and advanced real-time control in a single chip—a first for the industry. The TinyEngine NPU optimises deep learning tasks, ensuring faster and more efficient AI inference at the edge.

To support developers, TI offers the CCStudio integrated development environment (IDE), which includes generative AI tools for coding, debugging, and system setup. Engineers can use plain language to speed up development. The CCStudio Edge AI Studio also provides over 60 pre-trained AI models and application examples, covering motor control, automotive systems, wireless devices, and more. These resources help developers quickly deploy edge AI in various devices.

TI's broader goal is to expand edge AI adoption in everyday electronics. The company highlights use cases like health monitors, smart circuit breakers, and advanced robotics. By integrating dedicated AI hardware and development tools, TI aims to simplify the process of embedding intelligence into smaller, power-efficient devices.

The MSPM0G5187 and AM13Ex MCUs bring dedicated AI acceleration to low-power and real-time applications. With TinyEngine, developers gain improved performance and energy savings for edge AI tasks. TI's ecosystem, including pre-built models and generative AI tools, further lowers the barrier to deploying intelligent features in embedded systems.

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