@janjongboom mentioned at EI Image 2022 JAX. (If I read the Quickstart it is Numpy for CPU, GPU and TPU.) @janjongboom said it is a Python to graph compiler and gives the possibility to translate Python code to Tensorflow Lite and finally to source code, by using the EON tuner. Correct?
Am I also correct that the final goal is to get a Tensorflow Lite signal processing block that we can plug into together with your neural network block into the code?
I am starting to write a custom processing block to extract specific features, together with an event detection algorithm. I am using mainly numpy and scipy. Does it make sense to use JAX where it is possible? If yes, what do I need to take into account during the coding?
We’re currently building some building blocks in the SDK to make it easier to port these functions, and then hook it into EON Compiler to get on-device source code.
I am also not finished yet with the custom DSP block But I am early phase of development, so it is a good idea to make the blocks future-proof.
Keep me posted!