Running machine learning models on tiny hardware is all about trade-offs. Since clock cycles and memory are limited, TinyML developers often have to decide whether to trade model accuracy for improved performance or reduced memory use.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/better-insight-in-model-optimizations/