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Neural networks based on nanodevices, such as metal oxide memristors, phase change memories, and flash memory cells, have generated considerable interest for their increased energy efficiency and density in comparison to graphics processing units (GPUs) and central processing units (CPUs). Though immense acceleration of the training process can be achieved by leveraging the fact that the time complexity of training does not scale with the network size, it is limited by the space complexity ofdoi:10.3389/fnins.2019.00793 pmid:31447628 pmcid:PMC6691093 fatcat:zv3oh4habrbvtp6ese2ebeb4ym