PROJECT TITLE :

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

ABSTRACT:

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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