Realizing Neural Decoder at the Edge with Ensembled BNN
Information Theory
2021-06-21 v1 Signal Processing
math.IT
Abstract
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.
Cite
@article{arxiv.2106.09925,
title = {Realizing Neural Decoder at the Edge with Ensembled BNN},
author = {Devannagari Vikas and Nancy Nayak and Sheetal Kalyani},
journal= {arXiv preprint arXiv:2106.09925},
year = {2021}
}