Presentation
CREST-CiM: Cross-Coupling-Enhanced Differential STT-MRAM for Robust Computing-in-Memory in Binary Neural Networks
DescriptionWe 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 64x64 arrays shows up to 3.4x 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 <1% 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 2T-2MTJ baseline.
Event Type
Research Manuscript
TimeTuesday, June 2410:45am - 11:00am PDT
Location3003, Level 3
Design
DES2A: In-memory and Near-memory Computing Circuits
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