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CrossNets: High-Performance Neuromorphic Architectures for CMOL Circuits
2003
Annals of the New York Academy of Sciences
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We are developing a family of distributed crossbar network ("CrossNet") architectures that allow to combine high connectivity of neuromorphic circuits with high component density. ...
Neuromorphic networks with their defect tolerance seem the most natural way to address these problems. ...
CrossNets The somas (Fig. 3d ) may be implemented as MOSFET voltage amplifiers with a sigmoid-type saturation ("activation") function v j = g(u j ) common for the fire-rate models of neural networks. ...
doi:10.1196/annals.1292.010
pmid:14976016
fatcat:fo56kqtpqzbvppr2f7hpiyrpa4
Neuromorphic architectures for nanoelectronic circuits
2004
International journal of circuit theory and applications
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This paper reviews recent important results in the development of neuromorphic network architectures ('CrossNets') for future hybrid semiconductor=nanodevice-integrated circuits. ...
Moreover, CrossNets can also be trained to work as classiÿers by the faster error-backpropagation method, despite the absence of a layered structure typical for the usual neural networks. ...
CROSSNETS We have proposed [14] [15] [16] [17] a family of neuromorphic circuits, called Distributed Crossbar Networks ('CrossNets'), whose topology is uniquely suitable for CMOL implementation. ...
doi:10.1002/cta.282
fatcat:olaznxtw4fehvb73tvyoccdyly
A Review on CMOL Cell Assignment Problem
2012
IOSR Journal of Engineering
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CMOL cell assignment Problem (BP) is a telecommunication problem and this problem is very complex due to its constraint. So this problem needs to be resolved. ...
nanoelectronics has made tremendous progress, with advances in novel nanodevices, nano-circuits, nano-crossbar arrays , manufactured by nanoimprint lithography, CMOS/nano co-design architectures, and applications ...
[14] Developed a neuromorphic network ("CrossNet") architectures, in which neural cell bodies were implemented in CMOS. ...
doi:10.9790/3021-0204563567
fatcat:tvmk44uaafasnjgfamp5ampkni
3D hybrid CMOS/memristor circuits: Basic principle and prospective applications
2012
COMMAD 2012
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This paper is a brief overview of hybrid CMOS/memristor circuits and their applications for digital memories, programmable logic, and artificial neuromorphic networks. ...
Likharev, "CrossNets: Neuromorphic
hybrid
CMOS/nanoelectronic
networks",
Science of Advanced Materials, vol. 3, pp.
322-331, Jun. 2011. ...
that can directly benefit from hybrid circuit include those demanding significant amounts of memory access and/or relying of low-precision data (such as imaging, networking, and neuromorphic computing ...
doi:10.1109/commad.2012.6472340
fatcat:fapcmob5sjfelcchfzj6fbd4fa
Memory Technologies for Neural Networks
2015
2015 IEEE International Memory Workshop (IMW)
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Similarly to traditional memory applications, device density is one of the most essential metrics for large-scale artificial neural networks. ...
We then discuss the recent progress toward CrossNet implementation, in particular the experimental results for simple networks with crossbar-integrated resistive switching (memristive) metal oxide devices ...
After customization for analog state tuning of each cell, this technology may be used for artificial neural network applications. ...
doi:10.1109/imw.2015.7150295
fatcat:vjffk2d56vfyra65fuev2f2leu
Prospects for the development of digital CMOL circuits
2007
2007 IEEE International Symposium on Nanoscale Architectures
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This is a preliminary analysis of prospects and options for the development of hybrid CMOS/ nanoelectronic integrated circuits, in particular those of the "CMOL" variety. ...
We believe that CMOL technology is the most natural (and possibly the only practicable) way to extend the Moore's Law to the next 10 to 15 years, well beyond the 10-nm frontier. ...
(We will focus on digital circuits only, because studies of neuromorphic networks, which may eventually become the main application of the hybrid integrated circuits, are still in infancy, and their impact ...
doi:10.1109/nanoarch.2007.4400865
dblp:conf/nanoarch/StrukovL07
fatcat:kjyvuiwdjbd53oecr62uq5dqme
Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
2019
Materials
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These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. ...
Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. ...
Other alternative architectures for neuromorphic structures based on 3D integration of CMOS neurons and memristive synapses have been proposed as CrossNets [148] . ...
doi:10.3390/ma12172745
pmid:31461877
pmcid:PMC6747825
fatcat:bt6hgscpczd2ldc4xc6np7wsvu
Electronics Below 10 nm
[chapter]
2003
Nano and Giga Challenges in Microelectronics
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networks capable of advanced image recognition and more intelligent information processing tasks. ...
as well as new concepts for nanometer-scalable memory cells. ...
Figures 26 and 27 show the general structure, and two most promising species of the socalled distributed crossbar arrays for neuromorphic networks ("CrossNets") based on such switches [291] . ...
doi:10.1016/b978-044451494-3/50002-0
fatcat:mnnrwqks5bekpb2kcqo3tcbxzu
Design and defect tolerance beyond CMOS
2008
Proceedings of the 6th IEEE/ACM/IFIP international conference on Hardware/Software codesign and system synthesis - CODES/ISSS '08
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of application domains currently employing CMOS circuits. ...
domains for these technologies, and new opportunities that they may bring forward in defect tolerance design. ...
(iv) Mixed-signal neuromorphic CMOL networks ("CrossNets" [15, 16, 31, 39, 62] ) may provide extremely high performance for certain advanced information processing tasks such as pattern classification ...
doi:10.1145/1450135.1450187
dblp:conf/codes/HuKLNBW08
fatcat:fdscygyokjelxmlzrrlvomle6u
Artificial neural networks in hardware: A survey of two decades of progress
2010
Neurocomputing
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We specifically discuss, in detail, neuromorphic designs including spiking neural network hardware, cellular neural network implementations, reconfigurable FPGA based implementations, in particular, for ...
Hardware neural network Neurochip Parallel neural architecture Digital neural design Analog neural design Hybrid neural design Neuromorphic system FPGA based ANN implementation CNN implementation RAM based ...
Neuro-morphic Mixed-Signal CMOL Circuits (known as ''CrossNets'') [269] [270] [271] [272] [273] are the first results of an active research by K. ...
doi:10.1016/j.neucom.2010.03.021
fatcat:regzu6sshvekzd5wxcuaiytgqu
A compound memristive synapse model for statistical learning through STDP in spiking neural networks
2014
Frontiers in Neuroscience
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Therefore, the compound memristive synapse may provide a synaptic design principle for future neuromorphic architectures. ...
Memristors have recently emerged as promising circuit elements to mimic the function of biological synapses in neuromorphic computing. ...
for preparatory studies on robustness to device variability, and David Kappel as well as two anonymous reviewers for helpful comments on the manuscript. ...
doi:10.3389/fnins.2014.00412
pmid:25565943
pmcid:PMC4267210
fatcat:kbaoyd665bcwtox6zpnhkkzwc4