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.