Tuning model performance with the EON Tuner
In this recipe, we will use the Edge Impulse EON Tuner to find the best feature extraction method and ML architecture for KWS on the Arduino Nano.
Getting ready
Developing the most efficient ML pipeline for a given target platform is always challenging. One way to do this is through iterative experiments. For example, we can evaluate how some target metrics (latency, memory, and accuracy) change depending on the input feature generation and the model architecture. However, this process is time-consuming because several combinations need to be tested and evaluated. Furthermore, this approach requires familiarity with digital signal processing and NN architectures to know the parameters to tune.
The Edge Impulse EON Tuner (https://docs.edgeimpulse.com/docs/eon-tuner) is a powerful tool designed to automate discovering the most optimal ML solution for a given target platform. Unlike traditional AutoML tools focusing solely...
