@article{oai:kyutech.repo.nii.ac.jp:00007061,
 author = {Yoshimoto,  Yuma and Tamukoh, Hakaru and 田向, 権},
 issue = {2},
 journal = {Journal of Robotics and Mechatronics},
 month = {Apr},
 note = {In this study, with the aim of installing an object recognition algorithm on the hardware device of a service robot, we propose a Binarized Dual Stream VGG-16 (BDS-VGG16) network model to realize high-speed computations and low power consumption. The BDS-VGG16 model has improved in terms of the object recognition accuracy by using not only RGB images but also depth images. It achieved a 99.3% accuracy in tests using an RGB-D Object Dataset. We have also confirmed that the proposed model can be installed in a field-programmable gate array (FPGA). We have further installed BDS-VGG16 Tiny, a small BDS-VGG16 model in XCZU9EG, a system on a chip with a CPU and a middle-scale FPGA on a single chip that can be installed in robots. We have also integrated the BDS-VGG16 Tiny with a robot operating system. As a result, the BDS-VGG16 Tiny installed in the XCZU9EG FPGA realizes approximately 1.9-times more computations than the one installed in the graphics processing unit (GPU) with a power efficiency approximately 8-times higher than that installed in the GPU.},
 pages = {386--399},
 title = {FPGA Implementation of a Binarized Dual Stream Convolutional Neural Network for Service Robots},
 volume = {33},
 year = {2021},
 yomi = {タムコウ, ハカル}
}