CREST-CiM: Cross-Coupling-Enhanced Differential STT-MRAM for Robust Computing-in-Memory in Binary Neural Networks

We propose CREST-CiM, an STT-MRAM-based Computing-in-Memory (CiM) technique, targeted for binary neural networks. To circumvent the low-distinguishability issue in standard MRAM-based CiM, CREST-CiM utilizes two magnetic tunnel junctions (MTJs) to store +1 and -1 weights in a bitcell and cross-couples the MTJs, achieving a high-to-low current ratio of up to 8100 for a bit-cell. Our analysis for $64 \times 64$ arrays shows up to 3.4 x higher CiM sense-margin, 27.6% higher read-disturb-margin, and resilience to process variations, and other hardware non-idealities, albeit at the cost of just 7.9% overall-area overhead, and $\lt1 \%$ energy and latency overhead compared to a 2T-2MTJ-CiM design. Our system-level analysis for ResNet-18 trained on CIFAR-10 shows near-sofware inference accuracy with CREST-CiM, with 10.7% improvement over 2T2MTJ baseline.