Texas Instruments introduced two new microcontroller (MCU) families with edge artificial intelligence (AI) capabilities, supporting the company's commitment to enabling edge AI across its entire embedded processing portfolio. The MSPM0G5187 and AM13Ex MCUs integrate Texas Instruments' TinyEngine neural processing unit (NPU), a dedicated hardware accelerator for MCUs that optimizes deep learning inference operations to reduce latency and improve energy efficiency when processing at the edge. Texas Instruments' integrated TinyEngine NPU can run AI models with up to 90 times lower latency and more than 120 times lower energy utilization per inference than similar MCUs without an accelerator.
New general-purpose and real-time MCUs from Texas Instruments include the TinyEngine NPU to enable more efficient edge AI in any application, from simple to complex systems. With integrated generative AI in Texas Instruments' CCStudio IDE and more than 60 models and application examples in CCStudio Edge AI Studio, developers can quickly and easily add edge AI to any device. Texas Instruments' embedded processing portfolio is supported by a comprehensive development ecosystem, including the CCStudio integrated development environment (IDE). Its generative AI features allow engineers to use simple language to accelerate code development, system configuration and debugging through industry-standard agents and models paired with Texas Instruments data.
Texas Instruments is accelerating the adoption of edge AI in any electronic device, from real-time monitoring in wearable health monitors and home circuit breakers to physical AI in humanoid robots. The MSPM0G5187 Arm Cortex-M0+ MSPM0 MCU represents a fundamental shift for embedded designers, who can now bring edge AI to a wide range of simpler, smaller and more cost-effective applications. With local computation, the TinyEngine NPU executes computations required by neural networks in parallel to the primary CPU running application code.
Compared to similar MCUs without an accelerator, this hardware acceleration minimizes the flash memory footprint, lowers latency by up to 90 times per AI inference, and reduces energy utilization by more than 120 times per AI inference. Such levels of efficiency allow resource-constrained devices ? including portable, battery-powered products ?
to process AI workloads. At under USD 1 in 1,000-unit quantities, the MSPM0G5187 MCU reduces system and operating costs by offering an affordable alternative to other MCU or processor architectures. Motor control applications in appliances, robotics and industrial systems increasingly call for intelligent features such as adaptive control and predictive maintenance, but implementing these capabilities has historically required complex, multi-chip designs.
Texas Instruments' new AM13Ex MCUs are the industry's first to integrate a high-performance Arm Cortex-M33 core, TinyEngine NPU and advanced real-time control architecture into a single chip. This degree of integration enables designers to implement sophisticated motor control and AI features simultaneously without external components, lowering bill-of-materials costs by up to 30%. Key enhancements include the ability to maintain precise real-time control loops for up to four motors while the TinyEngine NPU runs adaptive control algorithms for load sensing and energy optimization, and an integrated trigonometric math accelerator that performs calculations 10 times faster than coordinate rotation digital computer (CORDIC) implementations, delivering more precise, responsive motor-control performance.
Both MCU families are supported by Texas Instruments' CCStudio Edge AI Studio, a free development environment that simplifies model selection, training and deployment across Texas Instruments' embedded processing portfolio. This edge AI toolchain gives engineers full flexibility to run AI models on Texas Instruments MCUs through either hardware or software implementations. There are more than 60 models and application examples available in the tool to help developers start deploying edge AI in any device, with additional tasks and models planned in the future.
Production quantities of the MSPM0G5187 MCU are available for purchase, with the AM13E23019 MCU available in preproduction quantities. Additional package and memory variants will be released by the end of 2026. Multiple payment and shipping options are available.
