Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Energy-Efficient Inference Accelerator for Memory-Augmented Neural
  Networks on an FPGA   Memory-augmented neural networks (MANNs) are designed for question-answering
tasks. It is difficult to run a MANN effectively on accelerators designed for
other neural networks (NNs), in particular on mobile devices, because MANNs
require recurrent data paths and various types of operations related to
external memory access. We implement an accelerator for MANNs on a
field-programmable gate array (FPGA) based on a data flow architecture.
Inference times are also reduced by inference thresholding, which is a
data-based maximum inner-product search specialized for natural language tasks.
Measurements on the bAbI data show that the energy efficiency of the
accelerator (FLOPS/kJ) was higher than that of an NVIDIA TITAN V GPU by a
factor of about 125, increasing to 140 with inference thresholding

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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
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The categories of this paper are: supr-con
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The categories of this paper are: physics
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The categories of this paper are: nlin
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: eess