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The crossbar array architecture with resistive synaptic devices is attractive for on-chip implementation of weighted sum and weight update in the neuro-inspired learning algorithms. This paper discusses the design challenges on scaling up the array size due to non-ideal device properties and array parasitics. Circuit-level mitigation strategies have been proposed to minimize the learning accuracy...
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