By Kim Mansfield
Embedded Firmware Engineer & AI Consultant
Modern "cloud-to-edge" AI platforms promise one-click deployments to microcontrollers. But if you have spent decades writing assembly and low-level C drivers, you know the reality: most embedded AI toolchains are too heavy.
When deploying machine learning models to space- and power-constrained hardware like the new Raspberry Pi Pico 2 W (RP2350) or traditional Cortex-M cores, developers are repeatedly running into the same roadblocks:
Massive Library Bloat: Monolithic SDKs drag in hundreds of kilobytes of unused operator kernels, bloated abstraction layers, and hidden heap allocations.
