Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey
  and Future Directions   In the past decade, Convolutional Neural Networks (CNNs) have demonstrated
state-of-the-art performance in various Artificial Intelligence tasks. To
accelerate the experimentation and development of CNNs, several software
frameworks have been released, primarily targeting power-hungry CPUs and GPUs.
In this context, reconfigurable hardware in the form of FPGAs constitutes a
potential alternative platform that can be integrated in the existing deep
learning ecosystem to provide a tunable balance between performance, power
consumption and programmability. In this paper, a survey of the existing
CNN-to-FPGA toolflows is presented, comprising a comparative study of their key
characteristics which include the supported applications, architectural
choices, design space exploration methods and achieved performance. Moreover,
major challenges and objectives introduced by the latest trends in CNN
algorithmic research are identified and presented. Finally, a uniform
evaluation methodology is proposed, aiming at the comprehensive, complete and
in-depth evaluation of CNN-to-FPGA toolflows.

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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: stat
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn