Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Realizing Neural Decoder at the Edge with Ensembled BNN   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.

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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org