On MCUs, you rarely deploy a raw neural network alone. You deploy a pipeline: feature extraction + tiny model.

Quantization (int8) is the default, but it’s only part of the story—memory layout, operator support, and IO timing dominate real performance.

We recommend starting with a constrained target (SRAM/flash budget), then designing the model to fit—rather than trimming after the fact.

Measure energy per inference, not just accuracy; battery life is often the real KPI.

Key Takeaways

• Lower latency
• Offline inference
• Better battery life