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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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  • Upasana Sahu ,
  • Naven Sisodia ,
  • Janak Sharda ,
  • Pranaba Kishor Muduli ,
  • Debanjan Bhowmik
Upasana Sahu
Indian Institute of Technology Delhi

Corresponding Author:[email protected]

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Naven Sisodia
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Janak Sharda
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Pranaba Kishor Muduli
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Debanjan Bhowmik
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Abstract

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.