Embedded AI on
Microcontrollers
Running machine learning inference on STM32, ESP32, and ARM Cortex-M microcontrollers — the same hardware that manages sensors, actuators, and communication. No external AI accelerator required.
The Engineering Problem
MCU-class inference is not a matter of shrinking a cloud model and flashing it to hardware. The constraints are severe and non-negotiable: a microcontroller with 256KB of RAM cannot run a model that requires 4MB of working memory, regardless of how the problem is framed.
Effective embedded AI engineering starts from the hardware constraints and works backward to the model architecture. Target accuracy is balanced against memory footprint, inference latency, and power consumption from the beginning of the model development process — not at the end.
WIRL Engineering designs embedded AI systems where the MCU selection, firmware architecture, model design, and quantization strategy are co-optimized. Each component is chosen in the context of the others.
- Model architecture selection for MCU resource constraints
- TensorFlow Lite for Microcontrollers deployment
- INT8 and INT4 post-training quantization
- CMSIS-NN kernel optimization for Cortex-M targets
- Edge Impulse project development and export
- Bare-metal and RTOS inference integration
- Flash footprint and RAM profiling
- Inference latency benchmarking on target hardware
- Production firmware integration and testing
AI Integration for
Your Hardware Platform
Discuss your MCU target and application requirements with our engineering team.