Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Survey of FPGA Based Deep Learning Accelerators: Challenges and
  Opportunities   With the rapid development of in-depth learning, neural network and deep
learning algorithms have been widely used in various fields, e.g., image, video
and voice processing. However, the neural network model is getting larger and
larger, which is expressed in the calculation of model parameters. Although a
wealth of existing efforts on GPU platforms currently used by researchers for
improving computing performance, dedicated hardware solutions are essential and
emerging to provide advantages over pure software solutions. In this paper, we
systematically investigate the neural network accelerator based on FPGA.
Specifically, we respectively review the accelerators designed for specific
problems, specific algorithms, algorithm features, and general templates. We
also compared the design and implementation of the accelerator based on FPGA
under different devices and network models and compared it with the versions of
CPU and GPU. Finally, we present to discuss the advantages and disadvantages of
accelerators on FPGA platforms and to further explore the opportunities for
future research.

5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph