Embedded ML for All Developers

Over the next decade, embedded is going to experiencing the kind of innovation we haven't seen since the late 2000s when open wireless, protocols and cryptography (and as a result, 32-bit MCUs) were introduced. Today most people think about Machine Learning as highly complex, large, and extremely memory and compute hungry -- with clusters of GPUs/TPUs heating whole towns... Now the age of tinyML has come -- we can already run meaningful ML inference on Cortex-M equivalent hardware. Rapid improvements in modern 32-bit MCU compute power efficiency and math capabilities (FPU, vector extensions), together with advancements in neural operators, architecture and quantization along with better open source tooling like TensorFlow Lite Micro are making this possible. For example, we recently built a complete DSP, Anomaly Detection and NN classifier for complex events on real-time 3-axis accelerometer data in software on a standard Cortex-M4 in just 6.6 kB of RAM and 20 kB of Flash. We are experiencing the start of what I call the "3rd wave of embedded compute."