Browsing by Author "Vourkas, I."

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    Resistive Switching Behavior seen from the Energy Point of View
    (IEEE, 2018) Gomez Mir, Jorge Tomás; Abusleme Hoffman, Ángel Christian; Vourkas, I.; Rubio, A.
    The technology of Resistive Switching (RS) devices (memristors) is continuously maturing on its way towards viable commercial establishment. So far, the change of resistance has been identified as a function of the applied pulse characteristics, such as amplitude and duration. However, parameter variability holds back any universal approach based on these two magnitudes, making also difficult even the qualitative comparison between different RS material compounds. On the contrary, there is a relevant magnitude which is much less affected by device variability; the energy. In this direction, we doubt anyone so far has ever wondered "what is the quantitative effect of the injected energy on the device state?" Interestingly, a first step was made recently towards the definition of performance parameters for this emerging device technology, using as fundamental parameter the energy. In this work, we further elaborate on such ideas, proving experimentally that the "resistance change per energy unit" (dRidE) can be considered a significant magnitude in analog operation of bipolar memristors, being a key performance parameter worth of timely disclosure.
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    Towards memristive crossbar-based neuromorphic HW accelerators for signal processing
    (IEEE, 2017) Vourkas, I.; Abusleme Hoffman, Ángel Christian; Vasileiadis, N.; Sirakoulis, G. C.; Papamarkos, N.
    Research progress in neuromorphic hardware, capable of biological perception and cognitive information processing, is leading the way towards a revolution in computing technology. Current research efforts have focused mainly on resistive switching devices, the electronic analog of synapses in artificial neural networks (ANNs), and the crossbar nanoarchitecture, for its huge connectivity and maximum integration density. In this context, this work presents the design and simulation of a memristive crossbar-based ANN for text recognition tasks, implementing a novel computing algorithm. In such case study, important issues during the application mapping process are identified and properly addressed at device and circuit level. The computing capabilities of the proposed system are highlighted through SPICE-level circuit simulations, which show excellent agreement with theoretical simulation results.