Computing-in-Memory with SRAM and RRAM for Binary Neural Networks

Recent advances in deep learning have shown that Binary Neural Network (BNN) is able to provide a satisfying accuracy on various image datasets with a significant reduction in computation and memory cost. With both weights and activations binarized to +1 or -1 in BNNs, the high-precision multiply-and-accumulate (MAC) operations can be replaced by XNOR and bit-counting operations. In this work, we present two computing-in-memory (CIM) architectures with parallelized weighted-sum operation for accelerating the inference of BNN: 1) parallel XNOR-SRAM, where a customized 8T-SRAM cell is used as a synapse; 2) parallel XNOR-RRAM, where a customized bit-cell consisting of 2T2R cells is used as a synapse. For large-scale weight matrices in neural networks, the array partition is necessary, where multi-level sense amplifiers (MLSAs) are employed as the intermediate interface for accumulating partial weighted sums. We explore various design options with different sub-array sizes and sensing bit-levels. Simulation results with 65nm CMOS PDK and RRAM models show that the system with 128×128 sub-array size and 3-bit MLSA can achieve 87.46% for an inspired VGG-like network on CIFAR-10 dataset, showing less than 1% degradation compared to the ideal software accuracy. The estimated energy-efficiency of XNOR-SRAM and XNOR-RRAM shows ~30× improvement compared to the corresponding conventional SRAM and RRAM architectures with sequential row-by-row read-out.