2,184 research outputs found
Nonphotolithographic nanoscale memory density prospects
Technologies are now emerging to construct molecular-scale electronic wires and switches using bottom-up self-assembly. This opens the possibility of constructing nanoscale circuits and memories where active devices are just a few nanometers square and wire pitches may be on the order of ten nanometers. The features can be defined at this scale without using photolithography. The available assembly techniques have relatively high defect rates compared to conventional lithographic integrated circuits and can only produce very regular structures. Nonetheless, with proper memory organization, it is reasonable to expect these technologies to provide memory densities in excess of 10/sup 11/ b/cm/sup 2/ with modest active power requirements under 0.6 W/Tb/s for random read operations
Memristor models for machine learning
In the quest for alternatives to traditional CMOS, it is being suggested that
digital computing efficiency and power can be improved by matching the
precision to the application. Many applications do not need the high precision
that is being used today. In particular, large gains in area- and power
efficiency could be achieved by dedicated analog realizations of approximate
computing engines. In this work, we explore the use of memristor networks for
analog approximate computation, based on a machine learning framework called
reservoir computing. Most experimental investigations on the dynamics of
memristors focus on their nonvolatile behavior. Hence, the volatility that is
present in the developed technologies is usually unwanted and it is not
included in simulation models. In contrast, in reservoir computing, volatility
is not only desirable but necessary. Therefore, in this work, we propose two
different ways to incorporate it into memristor simulation models. The first is
an extension of Strukov's model and the second is an equivalent Wiener model
approximation. We analyze and compare the dynamical properties of these models
and discuss their implications for the memory and the nonlinear processing
capacity of memristor networks. Our results indicate that device variability,
increasingly causing problems in traditional computer design, is an asset in
the context of reservoir computing. We conclude that, although both models
could lead to useful memristor based reservoir computing systems, their
computational performance will differ. Therefore, experimental modeling
research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio
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