Efficiency of Double-Barrier Magnetic Tunnel Junction-Based Digital eNVM Array for Neuro-Inspired Computing
نویسندگان
چکیده
This brief deals with the impact of spin-transfer torque magnetic random access memory (STT-MRAM) cell based on double-barrier tunnel junction (DMTJ) performance a two-layer multilayer perceptron (MLP) neural network. The DMTJ-based is benchmarked against conventional single-barrier MTJ (SMTJ) counterpart by means comprehensive evaluation carried out through state-of-the-art device-to-algorithm simulation framework. benchmark MNIST handwritten dataset, Verilog-A compact models developed our group, and 0.8 V FinFET technology. Our results point that use STT-MRAM cells to implement digital embedded non-volatile (eNVM) synaptic core allows write/read energy latency improvements about 53%/61% 66%/17%, respectively, as compared SMTJ-based equivalent design. achieved ensuring reduced area footprint learning accuracy 91%. Such make good eNVM option for neuro-inspired computing.
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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.2023.3240474