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  1. Home
  2. Indian Institute of Technology Madras
  3. Publication2
  4. Realizing Neural Decoder at the Edge with Ensembled BNN
 
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Realizing Neural Decoder at the Edge with Ensembled BNN

Date Issued
01-10-2021
Author(s)
Vikas, Devannagari
Nayak, Nancy
Sheetal Kalyani 
Indian Institute of Technology, Madras
DOI
10.1109/LCOMM.2021.3102319
Abstract
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.
Volume
25
Subjects
  • computation

  • deep learning

  • memory efficiency

  • Neural decoding

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