Palimpsestos e sobrevivências: o ser e o sentido na filosofia e na literatura

Klein, Kelvin Falcão

doi:10.5212/uniletras.v.31i2.159169

Cited by 1 publication

(2 citation statements)

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“…This leads to very long learning times for large circuits. We think that a parallel learning capability is one of the essential requirements to build neuromorphic machines for practical applications, and promises higher performance than CMOS based computing systems [34,35]. For the crossbar architecture based on memristors, a parallel learning process could potentially be carried out, as their conductance would not be updated if the potential difference between their two terminals is lower than a given threshold voltage [17,18].…”

Section: Comparison With Previous Nanodevice Based Crossbar Architectmentioning

confidence: 99%

“…Therefore, one of the most efficient solutions would be to use the low threshold voltage (e.g. 0.7 V) of memristors to control the parallel learning [18,35] or programming [36]. However, this method leads to important cross-talk effects, as the threshold voltages of memristors could be different from each other and difficult to precisely control due to nanofabrication issues.…”

Section: Comparison With Previous Nanodevice Based Crossbar Architect...mentioning

confidence: 99%

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Nanotube devices based crossbar architecture: toward neuromorphic computing

Zhao¹,

Agnus²,

Derycke³

et al. 2010

Nanotechnology

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Nanoscale devices such as carbon nanotube and nanowires based transistors, memristors and molecular devices are expected to play an important role in the development of new computing architectures. While their size represents a decisive advantage in terms of integration density, it also raises the critical question of how to efficiently address large numbers of densely integrated nanodevices without the need for complex multi-layer interconnection topologies similar to those used in CMOS technology. Two-terminal programmable devices in crossbar geometry seem particularly attractive, but suffer from severe addressing difficulties due to cross-talk, which implies complex programming procedures. Three-terminal devices can be easily addressed individually, but with limited gain in terms of interconnect integration. We show how optically gated carbon nanotube devices enable efficient individual addressing when arranged in a crossbar geometry with shared gate electrodes. This topology is particularly well suited for parallel programming or learning in the context of neuromorphic computing architectures.

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Section: Comparison With Previous Nanodevice Based Crossbar Architectmentioning

confidence: 99%