  When a neural network (NN) is used to decode a polar code, its training
complexity scales exponentially as the code block size (or to be precise, as a
number of message bits) increases. Therefore, existing solutions that use a
neural network for polar decoders are stuck with short block sizes like 16 or
32. Despite the fact that the NN training is very complex for long polar codes,
the NN decoding gives the better latency and its performance is potentially
close to the maximum likelihood (ML). In this paper, we describe an efficient
algorithm to create the NN decoding for a polar code of any size with the
initial performance that is equal or better than that of successive cancelation
(SC). Therefore, it creates an opportunity to design the NN based decoding with
the performance that is as close to the ML, as the training time allows.
