An end-to-end, production-ready TinyML pipeline to simultaneously estimate battery State of Charge (SoC) and State of Health (SoH) on low-power microcontrollers. Features Quantization-Aware Training (QAT) to compress a deep regression model down to 6.2 KB for bare-metal C++ BMS/EV edge hardware deployment.

Complete flow for keyword spotting on microcontrollers. From data collection to data preparation to training and deployment.

Not every application can tolerate the latency, bandwidth cost, or privacy exposure of cloud round-trips for every inference. Edge AI puts intelligence directly on the microcontroller.

Inference at the Edge. Train on desktop, deploy as pure C.

Implementation of Hasaki (Neural network CLI tool in C++ with configurable layers and activation functions)