Learn how to implement Artificial Intelligence in embedded systems. From neural networks and edge AI to image, audio, and sensor data processing with microcontrollers.
Master the STM32 X-CUBE-AI compiler. Deploy a Keras CNN model for Human Activity Recognition (HAR) onto the STM32U5, bypassing TFLM interpreter overhead for faster, leaner embedded AI.
TinyML Lab 2: Deploying Human Activity Recognition with X-CUBE-AI Read More »
Hands-on TinyML deployment guide. Learn model quantization in Python and deploy a sine wave predictor using TensorFlow Lite Micro with Arduino IDE.
TinyML Lab 1: Deploying Sine Prediction with TensorFlow Lite Micro Read More »
Understand the basics of TinyML and AIoT. Learn why moving AI to the edge is crucial for IoT and explore the top tools: Edge Impulse, X-CUBE-AI, and CMSIS-NN
TinyML & AIoT Introduction: Ecosystems, Workflow, and Tools Read More »
