Edge AI on Microcontroller Units (MCUs): A Deep Dive into Software Integration

Edge AI on MCUs is transforming how we interact with technology, from smart home devices to industrial IoT applications. With the increasing need for real-time, power-efficient processing, Edge AI has found its place on microcontroller units, bridging the gap between low-power devices and intelligent computation. In this blog, we will explore the software stack, tools, and methodologies that make this integration possible.

Understanding Edge AI on MCUs

To understand how Edge AI operates on MCUs, it is crucial to have a foundational knowledge of what constitutes an MCU and what "Edge AI" means. Microcontroller units are compact integrated circuits with a CPU, memory, and I/O peripherals designed to execute simple control functions. Edge AI refers to the ability to run machine learning models locally on a device without relying heavily on cloud-based computation.

The integration of AI into MCUs allows us to perform tasks like image recognition, anomaly detection, sensor data analysis, and natural language processing directly on edge devices. This approach offers significant advantages in terms of latency, power consumption, and privacy.

Key Software Components

Running AI models on MCUs requires an optimized software stack. This typically consists of the following key components:

Model Training Frameworks: The training of AI models is typically done on powerful machines using frameworks like TensorFlow, PyTorch, or Keras. These frameworks enable data preprocessing, model building, and fine-tuning to achieve high accuracy.
Model Conversion and Optimization Tools: The trained model, which is usually computationally heavy, needs to be converted and optimized for deployment on an MCU. Tools like TensorFlow Lite for Microcontrollers and ONNX Runtime provide a way to compress and quantize models, reducing their memory footprint and making them suitable for MCUs with limited resources.
Runtime Inference Engines: Once the model is optimized, it requires an inference engine to run on the MCU. Inference engines like CMSIS-NN (specifically optimized for ARM Cortex-M processors) and TensorFlow Lite Micro are commonly used. These inference engines provide the necessary libraries and optimized code to perform forward propagation of neural networks on constrained hardware.
Real-Time Operating Systems (RTOS): Many MCU-based applications require an RTOS to handle multiple tasks concurrently. Frameworks like FreeRTOS or Zephyr help in managing system-level scheduling, sensor data acquisition, communication stacks, and AI inference in a coordinated manner.
Embedded Software Development Kits (SDKs): MCU manufacturers provide SDKs that include optimized drivers, middleware, and tools necessary for edge AI development. For example, STMicroelectronics' STM32Cube.AI and NXP's eIQ are popular SDKs that simplify the process of deploying neural networks on their respective MCUs.

Optimizing AI Models for MCUs

One of the major challenges in deploying AI models on MCUs is the need to reduce computational complexity without sacrificing performance. Here are the major optimization techniques used:

Quantization: Quantization reduces the precision of model weights and activations, typically from 32-bit floating-point to 8-bit integers. This dramatically reduces both the memory requirements and computational load of the model.
Pruning: Pruning removes redundant connections or neurons from the network, simplifying the architecture. Techniques like structured and unstructured pruning help reduce model size while maintaining acceptable accuracy.
Knowledge Distillation: In this approach, a smaller model (student) learns from a larger, well-trained model (teacher). The distilled model retains much of the capability of the larger model but is significantly smaller in size, making it ideal for MCUs.
Model Partitioning: In scenarios where a single MCU cannot handle the entire model, partitioning allows for the distribution of computations across multiple MCUs or co-processors, balancing the load and reducing bottlenecks.

Toolchain for Edge AI on MCUs

The development toolchain for Edge AI on MCUs includes several essential components that assist in software development, debugging, and optimization:

Integrated Development Environments (IDEs): Tools like Keil MDK, IAR Embedded Workbench, and STM32CubeIDE offer a complete environment for coding, debugging, and programming the MCU.
Code Profiling and Debugging: Profiling tools are vital for assessing the performance of your AI model on the MCU. Tools such as ARM's Keil ULINK and Segger J-Link provide insight into the execution time and resource usage, allowing developers to fine-tune model parameters and code.
Simulation and Emulation Tools: Before deploying on hardware, tools like QEMU or vendor-specific simulators allow developers to emulate MCU behavior and validate AI inference results, ensuring a smoother transition to the physical device.

Case Study: TinyML for Anomaly Detection

Consider an industrial application where a vibration sensor is used to monitor machinery health. A TinyML model running on an STM32 MCU can be trained to detect abnormal patterns in the vibration signal, indicating potential mechanical faults.

Step 1: Data Collection: The vibration data is collected using accelerometers, which are interfaced with the STM32 MCU.
Step 2: Model Training: The data is then preprocessed, and a model is trained on a desktop environment using TensorFlow. The model learns to distinguish between normal and faulty vibrations.
Step 3: Model Optimization: The trained model is quantized using TensorFlow Lite, reducing its size from several megabytes to just a few kilobytes.
Step 4: Deployment: The optimized model is deployed to the STM32 MCU using STM32Cube.AI, which generates the necessary C code to run the inference.
Step 5: Inference: During operation, the model runs continuously, evaluating incoming sensor data and triggering an alert if an anomaly is detected.

Challenges and Future Directions

Memory and Compute Constraints: The most significant limitation of running AI on MCUs is the constrained environment. Memory management, power efficiency, and real-time constraints require clever optimization and model design.
Security Concerns: Running AI on edge devices also brings security challenges, such as ensuring the integrity of the AI model and protecting against data breaches. Embedded security features and secure boot mechanisms are being incorporated to address these concerns.
Emerging Trends: The future of Edge AI on MCUs is promising, with advancements in neuromorphic computing, better co-processors, and improved MCU architectures. Software tools are also evolving to make deployment faster and more efficient, while frameworks like TinyML are becoming the standard for edge intelligence.

Conclusion

Edge AI on MCUs represents a fusion of software engineering and hardware optimization, requiring deep collaboration across domains. The ability to run sophisticated AI models on tiny, low-power microcontrollers unlocks a wide range of applications, from smart appliances to health monitoring and industrial automation. Understanding the software components, tools, and optimization techniques is essential for anyone looking to delve into this field and contribute to the growth of AI at the edge.

If you're interested in getting hands-on with Edge AI, start by exploring platforms like Arduino Nano 33 BLE Sense, Raspberry Pi Pico, or STM32 series. These platforms provide extensive documentation, community support, and a good entry point for prototyping your ideas.

Next Steps

If you're ready to take the next step, try implementing a simple project, such as recognizing basic gestures using an accelerometer. Experiment with the tools and workflows described in this post, and you'll be on your way to mastering Edge AI on MCUs.