Realizing Neural Decoder at the Edge with Ensembled BNN

We propose extreme compression techniques like binarization, ternarization for Turbo code based Neural Decoders such as TurboAE. These methods reduce memory and computation by a factor of 64 and perform 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 a saving of 16 and 64 times in memory and computation respectively and help achieve performance the same as real-valued TurboAE.