@techreport{oai:ipsj.ixsq.nii.ac.jp:00195116,
 author = {Jiajun, Guo and Amr, Ashmawy and Thiem, Van Chu and Kiyofumi, Tanaka and Jiajun, Guo and Amr, Ashmawy and Thiem, Van Chu and Kiyofumi, Tanaka},
 issue = {36},
 month = {Mar},
 note = {Binarized Neural Network (BNN) is a promising technique for embedded inference hardware due to the small hardware cost, but the inference accuracy is degraded compared to a full-precision CNN. In this study, we show that the inference accuracy can be improved by using an ensemble of a few BNNs. In addition, we report our implementation on an FPGA., Binarized Neural Network (BNN) is a promising technique for embedded inference hardware due to the small hardware cost, but the inference accuracy is degraded compared to a full-precision CNN. In this study, we show that the inference accuracy can be improved by using an ensemble of a few BNNs. In addition, we report our implementation on an FPGA.},
 title = {High-Accuracy and Cost-Effective Neural Networks for Embedded Systems},
 year = {2019}
}