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 can be exploited to design efficient low-power, low-footprint extreme learning machine (ELM) architectures. In particular, we utilize the uncontrollable off-state resistance (Roff/HRS) spreads, of nanoscale filamentary-resistive memory devices, to realize random input weights and random hidden neuron biases; a characteristic requirement of ELM. We propose a novel 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 test (classification) and 2) SinC curve fitting (regression).