Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Nicoleta Cucu-Laurenciu"'
Publikováno v:
IEEE Open Journal of Nanotechnology, Vol 2, Pp 59-71 (2021)
In the paper we propose a reconfigurable graphene-based Spiking Neural Network (SNN) architecture and a training methodology for initial synaptic weight values determination. The proposed graphene-based platform is flexible, comprising a programmable
Externí odkaz:
https://doaj.org/article/7ec81bedb76d43cc833cfb0400c4660d
Publikováno v:
IEEE Open Journal of Nanotechnology, Vol 1, Pp 135-144 (2020)
To fully unleash the potential of graphene-based devices for neuromorphic computing, we propose a graphene synapse and a graphene neuron that form together a basic Spiking Neural Network (SNN) unit, which can potentially be utilized to implement comp
Externí odkaz:
https://doaj.org/article/65b73ceffc644f2e8f5565aaf2645047
Publikováno v:
IEEE Nanotechnology Magazine. 16:4-13
Publikováno v:
IEEE Transactions on Nanotechnology, 20
McCulloch-Pitts neuron structures are comprised of a number of synaptic inputs and a decision element, called soma. In this paper, we propose a 5-bit Graphene Nanoribbon (GNR)-based DAC to fulfill the role of the summation element featuring programma
Autor:
Said Hamdioui, Sorin Cotofana, Christoph Adelmann, Florin Ciubotaru, Frederic Vanderveken, Nicoleta Cucu-Laurenciu, Abdulqader Mahmoud
Publikováno v:
GLSVLSI 2022-Proceedings of the Great Lakes Symposium on VLSI 2022
In the early stages of a novel technology development, it is difficult to provide a comprehensive assessment of its potential capabilities and impact. Nevertheless, some preliminary estimates can be drawn and are certainly of great interest and in th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e3e44016df62c799fd022be6cc84c51
http://arxiv.org/abs/2204.10732
http://arxiv.org/abs/2204.10732
Publikováno v:
ACM Journal on Emerging Technologies in Computing Systems, 17(4)
Design and implementation of artificial neuromorphic systems able to provide brain akin computation and/or bio-compatible interfacing ability are crucial for understanding the human brain’s complex functionality and unleashing brain-inspired comput
Publikováno v:
IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 66(5)
In this paper, we augment a trapezoidal Quantum Point Contact topology with top gates to form a butterfly Graphene Nanoribbon (GNR) structure and demonstrate that by adjusting its topology, its conductance map can mirror basic Boolean functions, thus
Publikováno v:
IEEE Transactions on Nanotechnology, 18
As CMOS feature size is reaching atomic dimensions, unjustifiable static power, reliability, and economic implications are exacerbating, thereby prompting for conducting research on new materials, devices, and/or computation paradigms. Within this co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47576cbb399ab95c38807a3fa65d8af8
http://resolver.tudelft.nl/uuid:f13a288b-54d1-43eb-ba44-774b6815e5f1
http://resolver.tudelft.nl/uuid:f13a288b-54d1-43eb-ba44-774b6815e5f1
Publikováno v:
NANOARCH
With CMOS feature size heading towards atomic dimensions, unjustifiable static power, reliability, and economic implications are exacerbating, prompting for research on new materials, devices, and/or computation paradigms. Within this context, Graphe
Autor:
Valentin Savin, Sorin Cotofana, Nicoleta Cucu-Laurenciu, Joyan Chen, Oana Boncalo, Alexandru Amaricai
Publikováno v:
2015 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS).
In this paper we perform a fault tolerance assessment of flooded Low Density Parity Code (LDPC) decoders affected by probabilistic timing errors, characteristic to sub-powered CMOS circuits. We investigate the error correction capability - in terms o