A rigorous technical overview of deploying machine learning on microcontrollers governed by IEC 62304 and FDA guidelines.
Training a high-accuracy PyTorch model on GPU clusters is fundamentally different from ensuring that model executes deterministically within 64 KB of microcontroller memory without risking memory leaks or priority inversion.
Every quantized matrix multiplication must be tested against fixed-point overflow edge cases. IEC 62304 compliance requires 100% statement and branch coverage of the embedded inference runtime.
If the neural network encounters an out-of-distribution input or internal numerical instability, deterministic fallback routines must immediately take over to ensure patient monitoring is never interrupted.