$3 ESP32-C3 Runs TFLite for Industrial Anomaly Detection
blaizedsouza · x · 2026-08-25
This article presents an ultra-low-cost edge AI use case: using a $3 ESP32-C3 chip to detect machine failures.
- Hardware Base: The ESP32-C3 features a 160 MHz RISC-V core and 400 KB SRAM, sufficient for running 10-20 KB anomaly detection models.
- Workflow: Collect normal data -> Train autoencoder -> Quantize to INT8 -> Run inference on ESP32-C3 -> Report anomaly scores via MQTT.
- Application: Using sliding sensor windows, it runs inference locally and sends scores over Wi-Fi. This is ideal for motors, pumps, and fans where deploying many cheap monitoring nodes is preferred over a single expensive system.
The article provides a step-by-step guide from ESP-IDF setup to data collection.
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