XNOR-Bitcount Operation Exploiting Computing-In-Memory With STT-MRAMs
نویسندگان
چکیده
This brief presents an energy-efficient and high-performance XNOR-bitcount architecture exploiting the benefits of computing-in-memory (CiM) unique properties spin-transfer torque magnetic RAM (STT-MRAM) based on double-barrier tunnel junctions (DMTJs). Our work proposes hardware algorithmic optimizations, benchmarked against a state-of-the-art CiM-based design. Simulation results show that our optimization reduces storage requirement (–50%) for each operation. The proposed improves execution time energy consumption by about 30% (78%) 26% (85%), respectively, single (5 sequential) 9-bit operations. As case study, solution is demonstrated shape analysis using bit-quads.
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ژورنال
عنوان ژورنال: IEEE Transactions on Circuits and Systems Ii-express Briefs
سال: 2023
ISSN: ['1549-7747', '1558-3791']
DOI: https://doi.org/10.1109/tcsii.2023.3241163