High area efficiency binary neural processing elements with time-domain signals using SFQ circuits

With the increasing processor performance requirements of convolutional neural networks, we believe that research on superconducting neural networks based on single flux quantum (SFQ) circuits holds significant potential. However, the typical multiply and accumulate operations used in neural networks often lead to a significant increase in circuit area due to the limitations of the integration density of the current SFQ circuit fabrication process. In this work, we propose a binary neural processing element (PE) that utilizes time-domain signal expression. Time-domain signals express multi-bit binary data in time information, which reduces the number of data lines to decrease the circuit area. In the proposed binary neural PE, multiplication is achieved through exclusive-NOR gates and addition is realized by controlling the delay of time-domain signals. We designed and simulated a 3 x 3 convolution circuit using 10 Nb layer process with a critical current density of 10 kA cm-2. Compared to the convolution circuit implemented using conventional SFQ logic, the proposed circuit reduces the number of Josephson junctions by approximately 56% and decreases the area by about 60%.