185 research outputs found
Few-molecule reservoir computing experimentally demonstrated with surface enhanced Raman scattering and ion-gating stimulation
Reservoir computing (RC) is a promising solution for achieving low power
consumption neuromorphic computing, although the large volume of the physical
reservoirs reported to date has been a serious drawback in their practical
application. Here, we report the development of a few-molecule RC that employs
the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA)
detected by surface enhanced Raman scattering (SERS) with tungsten oxide
nanorod/silver nanoparticles (WOx@Ag-NPs). The Raman signals of the pMBA
molecules, adsorbed at the SERS active site of WOx@Ag-NPs, were reversibly
perturbated by the application of voltage-induced local pH changes in the
vicinity of the molecules, and then used to perform RC of pattern recognition
and prediction tasks. In spite of the small number of molecules employed, our
system achieved good performance, including 95.1% to 97.7% accuracy in various
nonlinear waveform transformations and 94.3% accuracy in solving a second-order
nonlinear dynamic equation task. Our work provides a new concept of molecular
computing with practical computation capabilities.Comment: 22 pages, 4 figure
A high-performance deep reservoir computing experimentally demonstrated with ion-gating reservoirs
While physical reservoir computing (PRC) is a promising way to achieve low
power consumption neuromorphic computing, its computational performance is
still insufficient at a practical level. One promising approach to improving
PRC performance is deep reservoir computing (deep-RC), in which the component
reservoirs are multi-layered. However, all of the deep-RC schemes reported so
far have been effective only for simulation reservoirs and limited PRCs, and
there have been no reports of nanodevice implementations. Here, as the first
nanodevice implementation of Deep-RC, we report a demonstration of deep
physical reservoir computing using an ion gating reservoir (IGR), which is a
small and high-performance physical reservoir. While previously reported
Deep-RC scheme did not improve the performance of IGR, our Deep-IGR achieved a
normalized mean squared error of 0.0092 on a second-order nonlinear
autoregressive moving average task, with is the best performance of any
physical reservoir so far reported. More importantly, the device outperformed
full simulation reservoir computing. The dramatic performance improvement of
the IGR with our deep-RC architecture paves the way for high-performance,
large-scale, physical neural network devices.Comment: 21 pages, 6 figure
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Minimizing the maximum delay for reaching consensus in quorum-based mutual exclusion schemes
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