embedded machine learning

**Embedded Machine Learning** is the **deployment and execution of ML models on embedded systems** — microcontrollers, DSPs, FPGAs, and specialized accelerators that are integrated into products, equipment, and industrial systems, running inference without cloud connectivity. **Embedded ML Stack** - **Hardware**: MCU (Cortex-M), DSP, FPGA, custom ASIC, neuromorphic chips. - **Runtime**: TensorFlow Lite Micro, ONNX Runtime, Apache TVM, vendor-specific SDKs. - **Optimization**: Quantization (INT8/INT4), pruning, operator fusion, memory planning. - **Integration**: Embedded ML models run alongside real-time control software (RTOS-based). **Why It Matters** - **Real-Time**: On-device inference enables microsecond-latency predictions for real-time control. - **Reliability**: No network dependency — works in air-gapped environments (clean rooms, secure facilities). - **Cost**: ML inference on a $1 MCU vs. streaming to cloud — orders of magnitude cheaper at scale. **Embedded ML** is **AI inside the machine** — running neural network inference directly on the embedded processors within industrial equipment and products.