Microcontrollers vs cloud: why AI is moving to the edge

Short summary

Modern IoT devices increasingly run local AI inference on microcontrollers with NPUs and DSPs, addressing bandwidth, latency, and privacy constraints. The hybrid approach sends summaries to cloud rather than raw data streams, with local models handling filtering and anomaly detection while cloud manages fleet analytics and long-term improvements. Success requires careful cost analysis, model versioning, OTA updates, and clear behavior definitions for network outages.

• •Edge inference reduces latency and bandwidth costs for IoT applications
• •Hybrid architecture: local MCUs for immediate decisions, cloud for fleet intelligence
• •Implementation requires model versioning, OTA updates, and network fallback planning

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