Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FPGA-based Neural Network Accelerator for Millimeter-Wave
  Radio-over-Fiber Systems   With the rapidly-developing high-speed wireless communications, the 60 GHz
millimeter-wave frequency range and radio-over-fiber systems have been
investigated as a promising solution to deliver mm-wave signals. Neural
networks have been studied to improve the mm-wave RoF system performances at
the receiver side by suppressing linear and nonlinear impairments. However,
previous neural network studies in mm-wave RoF systems focus on the off-line
implementation with high-end GPUs , which is not practical for low
power-consumption, low-cost and limited computation platform applications. To
solve this issue, we investigate neural network hardware accelerator
implementations using the field programmable gate array (FPGA), taking
advantage of the low power consumption, parallel computation capability, and
reconfigurablity features of FPGA. Convolutional neural network (CNN) and
binary convolutional neural network (BCNN) hardware accelerators are
demonstrated. In addition, to satisfy the low-latency requirement in mm-wave
RoF systems and to enable the use of low-cost compact FPGA devices, a novel
inner parallel optimization method is proposed. Compared with the embedded
processor (ARM Cortex A9) execution latency, the CNN/BCNN FPGA-based hardware
accelerator reduces their latency by over 92%. Compared with non-optimized FPGA
implementations, the proposed optimization method reduces the processing
latency by over 44% for CNN and BCNN. Compared with the GPU implementation, the
latency of CNN implementation with the proposed optimization method is reduced
by 85.49%, while the power consumption is reduced by 86.91%. Although the
latency of BCNN implementation with the proposed optimization method is larger
compared with the GPU implementation, the power consumption is reduced by
86.14%. The FPGA-based neural network hardware accelerators provide a promising
solution for mm-wave RoF systems.

22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph