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Ferrimagnetic Synapse Devices for Fast and Energy-Efficient On-Chip Learning on An Analog-Hardware Neural Network

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posted on 15.06.2021, 01:59 by Upasana SahuUpasana Sahu, Naven Sisodia, Janak Sharda, Pranaba Kishor Muduli, Debanjan Bhowmik
we have modeled domain-wall motion in ferrimagnetic and ferromagnetic devices through micro magnetics and shown that the domain-wall velocity can be 2–2.5X faster in the ferrimagnetic device compared to the ferromagnetic device. We also show that this velocity ratio is consistent with recent experimental findings Because of such a velocity ratio, when such devices are used as synapses in the crossbar-array-based fully connected network, our system-level simulation here shows that a ferrimagnet-synapse-based crossbar offers 4X faster (for the same energy efficiency) or 4X more energy-efficient (for the same speed) learning when compared to the ferromagnet-synapse-based crossbar.

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Email Address of Submitting Author

upasanasahu06@gmail.com

Submitting Author's Institution

Indian Institute of Technology Delhi

Submitting Author's Country

India