  We are trying to implement deep neural networks in the edge computing
environment for real-world applications such as the IoT(Internet of Things),
the FinTech etc., for the purpose of utilizing the significant achievement of
Deep Learning in recent years. Especially, we now focus algorithm
implementation on FPGA, because FPGA is one of the promising devices for
low-cost and low-power implementation of the edge computer. In this work, we
introduce Binary-DCGAN(B-DCGAN) - Deep Convolutional GAN model with binary
weights and activations, and with using integer-valued operations in forward
pass(train-time and run-time). And we show how to implement B-DCGAN on
FPGA(Xilinx Zynq). Using the B-DCGAN, we do feasibility study of FPGA's
characteristic and performance for Deep Learning. Because the binarization and
using integer-valued operation reduce the memory capacity and the number of the
circuit gates, it is very effective for FPGA implementation. On the other hand,
the quality of generated data from the model will be decreased by these
reductions. So we investigate the influence of these reductions.
