Design of Binary Convolution Operation Circuit for Binarized Neural Networks Using Single-Flux-Quantum Circuit

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 $ \times $ 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/cm² Nb process. The measurement results show that the BCOC can perform binary convolution operations with a kernel size of 3 $ \times $ 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.