LAUSR

Compute-in-memory (CiM) is an effective way to solve the memory wall issue. As a promising candidate for CiM, resistive random access memory (RRAM) has the advantages of non-volatile, fast operating speed, and programmable resistance. However, the resistance variation and poor retention will cause computation accuracy loss, especially in neural networks’ multiply-accumulate (MAC). In this brief, we propose a voltage-mode XNOR logic mapping scheme to implement MAC which could realize the linear change of the voltage on the source line (SL) of the RRAM array. The scheme was verified on a $32\times 32$ RRAM array with a binary neural network (BNN) for MNIST and CIFAR-10 classification. The implementation results show only 0.14% accuracy loss (MINST) compared with software implementation and an $8.7\times $ improvement in energy consumption compared with current-mode sensing. In addition, we further propose an energy-efficient parallel scheme to reduce the accuracy loss to 0.02% (MINST) and achieve a $4.34\times $ improvement in energy consumption.