Sensor data is typically preprocessed with DSP in tinyML applications. As engineers deploy neural networks on ever smaller processors, it is becoming necessary to tune DSP algorithms in order to fit within RAM or real-time processing constraints. But not all steps in a DSP pipeline are created equal! Knowing how to find sections to slim down can mean the difference between giving up a few percent of accuracy, and ending up with a model that’s no longer usable.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/become-a-dsp-tuning-master-and-build-more-efficient-neural-networks