TinyML and Model Quantization for Edge AI

TinyML brings machine learning to microcontrollers with severe memory (<256KB RAM) and compute constraints. Post-training quantization converts FP32 weights to INT8.

Key Uses:

Wake-word detection, predictive maintenance sensors, and always-on vision on embedded devices.

Hacks:

Dynamic range quantization and float16/INT8 mixed precision. Pruning (magnitude-based weight removal) followed by fine-tuning. Knowledge distillation from large teacher to tiny student model. Use TensorFlow Lite Micro or Edge Impulse for optimized deployment.

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