  In this work, we propose extreme compression techniques like binarization,
ternarization for Neural Decoders such as TurboAE. These methods reduce memory
and computation by a factor of 64 with a performance better than the quantized
(with 1-bit or 2-bits) Neural Decoders. However, because of the limited
representation capability of the Binary and Ternary networks, the performance
is not as good as the real-valued decoder. To fill this gap, we further propose
to ensemble 4 such weak performers to deploy in the edge to achieve a
performance similar to the real-valued network. These ensemble decoders give 16
and 64 times saving in memory and computation respectively and help to achieve
performance similar to real-valued TurboAE.
