Filters

2016 - 20202

More filters

2022 - 20232
Reset filters

Settings

DDC: 600 × Subject: computer simulation ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    On the Dynamical Regimes of Pattern-Accelerated Electroconvection
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2016) Davidson, Scott M.; Wessling, Matthias; Mani, Ali
    Recent research has established that electroconvection can enhance ion transport at polarized surfaces such as membranes and electrodes where it would otherwise be limited by diffusion. The onset of such overlimiting transport can be influenced by the surface topology of the ion selective membranes as well as inhomogeneities in their electrochemical properties. However, there is little knowledge regarding the mechanisms through which these surface variations promote transport. We use high-resolution direct numerical simulations to develop a comprehensive analysis of electroconvective flows generated by geometric patterns of impermeable stripes and investigate their potential to regularize electrokinetic instabilities. Counterintuitively, we find that reducing the permeable area of an ion exchange membrane, with appropriate patterning, increases the overall ion transport rate by up to 80%. In addition, we present analysis of nonpatterned membranes and find a novel regime of electroconvection where a multivalued current is possible due to the coexistence of multiple convective states.
  • Thumbnail Image
    Item
    Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zahari, Finn; Pérez, Eduardo; Mahadevaiah, Mamathamba Kalishettyhalli; Kohlstedt, Hermann; Wenger, Christian; Ziegler, Martin
    Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells. © 2020, The Author(s).