@article{oai:ynu.repo.nii.ac.jp:00011588,
 author = {Zongyuan, Li and Yuki Yamanashi and Nobuyuki Yoshikawa},
 issue = {4},
 journal = {IEEE Transactions on Applied Superconductivity},
 month = {Jan},
 note = {We design a binary convolution operation circuit (BCOC) using a single-flux-quantum circuit for high-speed and energy-efficient neural network. The proposed circuit is used for binary convolution operations using a convolution kernel size of 3 × 3, which accelerates the forward propagation process of a binary neural network (BNN). We analyze the binary convolution process and propose a bisection method for optimization. The BCOC is designed with a gate-level pipeline architecture and uses the bisection method for reduced number of pipeline stages. Thus, the circuit area of the BCOC is reduced by approximately 50% compared with that of a BCOC without the bisection method. We design the BCOC with 3270 Josephson junctions using a 10 kA/cm^2 Nb process. The measurement results show that the BCOC can perform binary convolution operations with a kernel size of 3 × 3. Compared to a CMOS circuit, BCOC increases the power efficiency by 3.9 times. In future research, we will build up a library of BNNs based on SFQ circuits to simulate various BNN structures.},
 title = {Design of Binary Convolution Operation Circuit for Binarized Neural Networks Using Single-Flux-Quantum Circuit},
 volume = {32},
 year = {2022}
}