@inproceedings{e10fb868db1d4e4491e1aca7e52d15c4,
title = "A CNN Accelerator on FPGA using Binary Weight Networks",
abstract = "At present, convolutional neural networks have good performance while performing the object recognition tasks, but it relies on GPUs to solve a large number of complex operations. Therefore, the hardware accelerator of the neural network has become a central topic in the hardware researchers. This letter presents the design of an FPGA-based neural network accelerator implemented on the Xilinx Zynq-7020 FPGA. We use the binary LeNet model to achieve 91% accuracy in the MNIST dataset and use binary AlexNet model to achieve 67% accuracy in the CIFAR-10 dataset. Meanwhile the hardware resource is only about 10% usage on FPGA of the original design.",
author = "Tsai, {Tsung Han} and Ho, {Yuan Chen}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 ; Conference date: 28-09-2020 Through 30-09-2020",
year = "2020",
month = sep,
day = "28",
doi = "10.1109/ICCE-Taiwan49838.2020.9258351",
language = "???core.languages.en_GB???",
series = "2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020",
}