Exploiting Intrinsic Variability of Filamentary Resistive Memory for Extreme Learning Machine Architectures


In this paper, we show for the first time how unavoidable device variability of emerging nonvolatile resistive memory devices will be exploited to style efficient low-power, low-footprint extreme learning machine (ELM) architectures. In explicit, we have a tendency to utilize the uncontrollable off-state resistance (Roff/HRS) spreads, of nanoscale filamentary-resistive memory devices, to comprehend random input weights and random hidden neuron biases; a characteristic demand of ELM. We tend to propose a completely unique RRAM-ELM architecture. To validate our approach, experimental data from different filamentary-resistive switching devices (CBRAM, OXRAM) are used for full-network simulations. Learning capability of our RRAM-ELM architecture is illustrated with the help of two real-world applications: 1) diabetes diagnosis take a look at (classification) and a couple of) SinC curve fitting (regression).

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