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Mastering TinyML

Collection of TinyML examples for popular MCU boards on popular frameworks (repositories).

Tiny Machine Learning (TinyML) is a subdomain of machine learning (ML) for low-end (less than 1 USD) and low-power (mW range) processors (MCU, DSP) constrainted by CPU, SRAM and FlashRAM and running on batteries. Applications are predictive maintenance, wake word, behavior detection ... More details on TinyML.org.

Coming soon ...

Boards

STM32

Espressif

  • Espressif ESP32 Eye
  • Espressif ESP32 Cam
  • Espressif ESP32-S3 (ESP NN)
  • TTGO-TBEAM-SUPREME-868-H661-2823 TTGO T-BEAM SUPREME SX1262 Meshtastic 868 MHz Lora WiFi BT5 GPS
  • TTGO-TBEAM-SUPREME-433-H664-2825 LILYGO T-BEAM SUPREME SX1262 433 MHz Meshtastic LoRa WiFi BT5 GPS
  • M5Stack ESP32 PSRAM Timer Camera X (OV3660)

Raspberry Pico

Others

Most of MCU Boards are available at FabMaSTIC fablab.

Power consumption measurement

TinyML Frameworks

  • TensorFlow Lite Micro (Edge Impulse, Imagimob)
  • MindSpore Lite
  • ST X-CUBE-AI (for STM32 MCU)
  • ST NanoEdgeAIStudio (for STM32 MCU)

Funding

This work is partially funded by Persyval Lab, MIAI and LIG Lab.

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