2024: The year when MCUs became AI-enabled

Artificial intelligence (AI) and machine learning (ML) technologies, once synonymous with large-scale data centers and powerful GPUs, are steadily moving toward the network edge via resource-limited devices like microcontrollers (MCUs). Energy-efficient MCU workloads are being melded with AI power to leverage audio processing, computer vision, sound analysis, and other algorithms in a variety of embedded applications.

Take the case of STMicroelectronics and its STM32N6 microcontroller, which features neural processing unit (NPU) for embedded inference. It’s ST’s most powerful MCU and carries out tasks like segmentation, classification, and recognition. Alongside this MCU, ST offers software and tools to lower the barrier to entry for developers to take advantage of AI-accelerated performance for real-time operating systems (RTOSes).

Figure 1 The Neural-ART accelerator in STM32N6 claims to deliver 600 times more ML performance than a high-end STM32 MCU today. Source: STMicroelectronics

Infineon, another leading MCU supplier, has also incorporated a hardware accelerator in its PSOC family of MCUs. Its NNlite neural network accelerator aims to facilitate new consumer, industrial, and Internet of Things (IoT) applications with ML-based wake-up, vision-based position detection, and face/object recognition.

Next, Texas Instruments, which calls its AI-enabled MCUs real-time microcontrollers, has integrated an NPU inside its C2000 devices to enable fault detection with high accuracy and low latency. This will allow embedded applications to make accurate, intelligent decisions in real-time to perform functions like arc fault detection in solar and energy storage systems and motor-bearing fault detection for predictive maintenance.

Figure 2 C2000 MCUs integrate edge AI hardware accelerators to facilitate smarter real-time control. Source: Texas Instruments

The models that run on these AI-enabled MCUs learn and adapt to different environments through training. That, in turn, helps systems achieve greater than 99% fault detection accuracy to enable more informed decision-making at the edge. The availability of pre-trained models further lowers the barrier to entry for running AI applications on low-cost MCUs.

Moreover, the use of a hardware accelerator inside an MCU offloads the burden of inferencing from the main processor, leaving more clock cycles to service embedded applications. This marks the beginning of a long journey for AI hardware-accelerated MCUs, and for a start, it will thrust MCUs into applications that previously required MPUs. The MPUs in the embedded design realm are also not fully capable of controlling design tasks in real-time.

Figure 3 The AI-enabled MCUs replacing MPUs in several embedded system designs could be a major disruption in the semiconductor industry. Source: STMicroelectronics

AI is clearly the next big thing in the evolution of MCUs, but AI-optimized MCUs have a long way to go. For instance, software tools and their ease of use will go hand in hand with these AI-enabled MCUs; they will help developers evaluate the embeddability of AI models for MCUs. Developers should also be able to test AI models running on an MCU in just a few clicks.

The AI party in the MCU space started in 2024, and 2025 is very likely to witness more advances for MCUs running lightweight AI models.

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