Computer Architectures for Nanotechnology: Towards Nanocomputing

Gamrat, Christian

doi:10.1007/978-3-540-28617-2_19

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Physically one crucial decision in neuromorphic computing architectures is the physical form of signals between the computing elements (neurons). Both Gamrat, 41 Morie et al, 42 and Rouw and Hoekstra 43 consider electrical spikes as the right coding method. Rouw and Hoekstra argue in favor of spike-based coding due to the inaccuracy in the amplitude of signals processed in nanoelectronics.…”

Section: Neuromorphic Architecturesmentioning

confidence: 98%

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Machine Learning: How It Can Help Nanocomputing

Uusitalo¹,

Peltonen²,

Ryhänen³

2011

Jnl of Comp & Theo Nano

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We examine possibilities for making advances in nanocomputing by bringing in ideas from the field of machine learning. The potential from combining machine learning with nanocomputing seems to be underutilized. We review three complementary approaches. Firstly, machine learning can be used in the different phases of developing complicated nanocomputing devices: in modeling, designing, constructing, and programming the devices. Secondly, machine learning methods implemented by nanocomputing hardware can be a competitive solution especially for specialized application areas like sensory information processing; working towards such implementations advances nanocomputing by guiding development of the nanocomponents and architectures required for such applications. Thirdly, nanotechnology enabled quantum computing can significantly increase our capacity to solve NP-complete optimization problems; although this increase is not specific to machine learning, several such problems occur in machine learning and artificial intelligence, hence solving such problems is a useful goal that partly motivates development of quantum computing. The main value of this paper is to provide new ideas for researchers working on nanocomputing, nanoarchitectures, development and design of nanoprocessors and other nanocomponents, or nanomanufacturing.

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Section: Neuromorphic Architecturesmentioning

confidence: 98%

“…41 CNNs with discrete time have also been proposed. 69 This would require synchronization of the whole circuit.…”

Section: Cellular Neural Network and Cellular Automatamentioning

confidence: 99%

Machine Learning: How It Can Help Nanocomputing

Uusitalo¹,

Peltonen²,

Ryhänen³

2011

Jnl of Comp & Theo Nano

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“…• Fluid axial conduction [24]; • Surface roughness [25][26][27]; • Gas rarefaction [28]; • Electro-osmotic effects [29,30].…”

Section: Introductionmentioning

confidence: 99%

Guidelines for the Determination of Single-Phase Forced Convection Coefficients in Microchannels

Morini¹,

Yang

2013

Journal of Heat Transfer

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This paper deals with the analysis of the main features of forced microconvection of liquid and gas flows through microchannels. A critical oveiyiew of the main effects that tends to play an important role in the determination of Nusselt number in microchannels is presented. Some experimental data obtained at the Microfluidics Lab of the University of Bologna together with the main results which appeared recently in the open literature both for liquids and gases are used in order to highlight the peculiar characteristics of the convective heat transfer through microchannels and to suggest the guidelines for a physically based interpretation to the experimental results. By means of speciflc examples, it is shown that the thermal behavior at microscale of gas and liquid flows through microchannels in terms of convective heat transfer coefficients can be strongly affected by scaling and micro-effects but also by practical issues linked to the geometry of the test rig, the real thermal boundary conditions, the presence of flttings, position and type of the sensors, and so on. All these aspects have to be taken into account during the data post processing in order to obtain a correct evaluation of the Nusselt numbers. It is also highlighted how it is always useful to couple to the experimental approach a complete computational thermal fluid-dynamics analysis of the whole tested microsystem in order to be able to recognize "a priori" the main effects which can play an important role on the convective heat transfer analysis. It is demonstrated in this paper that this "a priori" analysis is crucial in order to: (i) individuate the main parameters which influence the convective heat transfer coefficients (this is important for the development of new correlations); (ii) compare in a right way the conventional correlations with the experimental results.

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Computer Architectures for Nanotechnology: Towards Nanocomputing

Cited by 2 publications

References 9 publications

Machine Learning: How It Can Help Nanocomputing

Machine Learning: How It Can Help Nanocomputing

Guidelines for the Determination of Single-Phase Forced Convection Coefficients in Microchannels

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