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TinyML
Deploying and optimizing ML models on microcontrollers with a focus on speed and efficiency.
Overview
A microcontroller focused ML project implementing quantized operators and lightweight inference loops for extremely resource constrained devices. Includes latency and energy profiling to evaluate performance trade-offs between accuracy, model size, and runtime efficiency. Features deployment ready templates for Arduino Nano 33 BLE Sense and similar boards.
Key Features
1
Quantized kernels tailored for MCUs
2
Model size reduction and calibration
3
Latency and energy profiling tools
4
Board specific deployment templates
Technologies
C++ArduinoEmbeddedQuantizationMicrocontrollers