Programming fine-grained reconfigurable architectures

Described is a dynamically reconfigurable 8-function logic gate with seven double-gate carbon nanotube field-effect transistors which demonstrates p-type or n-type behaviour depending on the back-gate voltage. Through simulations, the gate is shown to operate at 20 GHz and has been used to build a 1-bit pipelined full adder using physically identical reconfigurable cells

Cellular automata (CA) are massively parallel systems where a regular grid of finite symbols is updated according to a synchronous application of the same local update rule everywhere. A closely related concept is that of Wang tiles where a local relation between neighboring symbols determines allowed combinations of symbols in the grid. In this tutorial we start with classical results on cellular automata, such as the Garden-of-Eden theorems, the Curtis-Hedlund-Lyndon-theorem and the balance property of surjective cellular automata. We then discuss Wang tiles and, in particular, the concept of aperiodicity and the undecidability of the domino problem. The domino problem is the decision problem to determine if a given Wang tile set admits any valid tilings of the grid. We relate Wang tiles to cellular automata, and establish a number of undecidability results for cellular automata.

Though confinement of spin waves (SWs) through nanochannels (NCs) is essential for the development of magnonic logic hardware, voltage-controlled reconfigurable SW nanochannels and logic devices have remained elusive. Here numerical micromagnetic simulations show that NCs of arbitrary shape and size, down to a few tens of nanometers, can be easily configured to transmit SWs with variable wave vectors as needed, even in the absence of a magnetic field. Operation of reconfigurable logic devices using the NCs is also demonstrated. This study is expected to have a large impact on the development of all-voltage-controlled low-power nanoscale magnonic devices.

In this paper, we propose a new method to obtain the transition rules of two-dimensional cellular automata (CA) that performs grayscale image processing. CA has the advantages of producing complex systems from the local interaction of simple elements, and has attracted increased research interest. The difficulty of designing CA's transition rules to perform a particular task has severely limited their applications. So, the evolutionary design of CA rules has been studied. In this method, an evolutionary algorithm was used to evolve CA. In recent years, this method has been applied to image processing. Rosin has studied the evolutionary design of two-dimensional CA to perform noise reduction, thinning and convex hulls. Batouche et al. and Slatnia et al. employed genetic algorithm to investigate the possibility of CA to perform edge detection. In the previous methods, 2-state CA was used for binary image processing. Unlike the previous methods, the present method uses 256-state CA rules to perform grayscale image processing. Gene Expression Programming (GEP) proposed by Ferreira is employed as a learning algorithm in which the chromosomes encode the transition rules as expression trees. Experimental results for the reduction of impulse noise, salt-and-pepper noise and gaussian noise show that the proposed method is equivalent to previous methods in performance and more than 100 times faster than the method proposed by Rosin. We show that the rule obtained by the proposed method employs symmetry-based strategy in the noise reduction process and this property can reduce complexity of CA.

CELLULAR AUTOMATA MODELLING APPROACH FOR URBAN GROWTH

In this article*, as a new mathematical approach to origin of the laws of nature, using a new basic algebraic axiomatic (matrix) formalism based on the ring theory and Clifford algebras (presented in Sec.2), “it is shown that certain mathematical forms of fundamental laws of nature, including laws governing the fundamental forces of nature (represented by a set of two definite classes of general covariant massive field equations, with new matrix formalisms), are derived uniquely from only a very few axioms”; where in agreement with the rational Lorentz group, it is also basically assumed that the components of relativistic energy-momentum can only take rational values. In essence, the main scheme of this new mathematical axiomatic approach to fundamental laws of nature is as follows. First based on the assumption of rationality of D-momentum, by linearization (along with a parameterization procedure) of the Lorentz invariant energy-momentum quadratic relation, a unique set of Lorentz invariant systems of homogeneous linear equations (with matrix formalisms compatible with certain Clifford, and symmetric algebras) is derived. Then by first quantization (followed by a basic procedure of minimal coupling to space-time geometry) of these determined systems of linear equations, a set of two classes of general covariant massive (tensor) field equations (with matrix formalisms compatible with certain Clifford, and Weyl algebras) is derived uniquely as well. Each class of the derived general covariant field equations also includes a definite form of torsion field appeared as generator of the corresponding field’ invariant mass. In addition, it is shown that the (1+3)-dimensional cases of two classes of derived field equations represent a new general covariant massive formalism of bispinor fields of spin-2, and spin-1 particles, respectively. In fact, these uniquely determined bispinor fields represent a unique set of new generalized massive forms of the laws governing the fundamental forces of nature, including the Einstein (gravitational), Maxwell (electromagnetic) and Yang-Mills (nuclear) field equations. Moreover, it is also shown that the (1+2)-dimensional cases of two classes of these field equations represent a new general covariant massive formalism of bispinor fields of spin-3/2 and spin-1/2 particles, corresponding (asymptotically) to the Dirac and Rarita–Schwinger equations. As a particular consequence, it is shown that a certain massive formalism of general relativity – with a definite form of torsion field appeared originally as the generator of gravitational field’s invariant mass – is obtained only by first quantization (followed by a basic procedure of minimal coupling to space-time geometry) of a certain set of special relativistic algebraic matrix equations. In addition, in agreement with recent astronomical data, a new particular form of massive particle is identified (as the gauge boson of U(1) symmetry group) with invariant mass: m_γ ≈ 4.90571×10^-50 kg, which is generated by a definite coupled torsion field of the background space-time geometry. Moreover, based on the definite mathematical formalism of this axiomatic approach, along with the C, P and T symmetries (represented basically by the corresponding quantum operators) of the fundamentally derived field equations, it has been concluded that the universe could be realized solely with the (1+2) and (1+3)-dimensional space-times (where this conclusion, in particular, is based on the T-symmetry of these equations). It is shown that CPT is the only combined form of C, P, and T symmetries of interacting fields. In addition, on the basis of these discrete symmetries of derived field equations, it has been also shown that only left-handed particle fields (along with their complementary right-handed fields) could be coupled to the corresponding (any) source currents. Furthermore, it has been shown that the metric of background space-time is diagonalized for the uniquely derived fermion field equations (defined and expressed solely in (1+2)-dimensional space-time), where this property generates a certain set of additional symmetries corresponding uniquely to the "SU(2)_L⊗U(2)_R" symmetry group for spin-1/2 fermion fields (representing “1+3” generations of four fermions, including a group of eight leptons and a group of eight quarks), and also the "SU(2)_L⊗U(2)_R" and SU(3) gauge symmetry groups for spin-1 boson fields coupled to the spin-1/2 fermionic source currents. Hence, along with the known elementary particles, eight new elementary particles, including four new charge-less right-handed spin-1/2 fermions (including two leptons and two quarks), a spin-3/2 fermion, and also three new spin-1 (massive) bosons are predicted uniquely by this new mathematical axiomatic approach. As a particular result, based on the definite formulation of the derived Maxwell (and Yang-Mills) field equations, it has been also concluded that magnetic monopoles could not exist in nature.* *Note: "See also new published version of this article at: https://inspirehep.net/record/1387680/ ”. Comments: 90 Pages. A summary of a submitted and accepted research project (On "Foundations of Physics"), Ramin Zahedi, Japan, 2012 - 2015. This article has been invited and presented at the following international conferences: - The 2016 SIAM International Conference on Mathematical Aspects of Materials Science, Philadelphia, USA, 2016. (https://www.siam.org/meetings/ms16) - The 17th International Conference on Quantum Foundations: Quantum and Beyond, International Centre for Mathematical Modeling in Physics, Engineering and Cognitive Sciences (ICMM), Linnaeus University, Sweden, 2016. (https://lnu.se/en/qb/) -The 4th International Conference on New Frontiers in Physics, CERN Organized and Supported Conference (Europe), ICNFP-2015. (https://indico.cern.ch/e/icnfp2015/) - The 2016 International Conference on Algebraic Geometry and Mathematical Physics, University of Tromsø, Norway, 2016. (https://site.uit.no/) - The XXXVII Max Born International Symposium, International Conference on Non-commutative Geometry, Quantum Symmetries and Quantum Gravity (II), Wroclaw University, Poland, 2016. (http://ift.uni.wroc.pl/~mborn37/) - The 2016 GRavitational-wave Astronomy International Conference in Paris, Institute d‘Astrophysique de Paris (IAP), The University of Paris VI - Sorbonne University, CNRS, LERU, EUA, Paris, France, 'Supported by the European Union's 7th Framework Program: FP7/PEOPLE-2011-CIG,‘ 2016. (http://www.iap.fr/vie_scientifique/ateliers/GravitationalWave/2016/scripts/abstract.aspx) - The 22nd Internnational Australian Institute of Physics Congress (AIP), University of Queensland, Brisbane, Australia, 2016. (http://appc-aip2016.org.au) - The 21st International Conference on General Relativity and Gravitation, Columbia University, New York, USA, 2016. (http://www.gr21.org). External URLs (including preprints and earlier published versions of this research article): https://cds.cern.ch/record/1980381/ , https://inspirehep.net/record/1387680/, https://indico.cern.ch/event/344173/session/22/contribution/422/attachments/1140145/1646101/R.a.Zahedi--OnDiscretePhysics-Jan.2015-signed.pdf, http://eprints.lib.hokudai.ac.jp/dspace/handle/2115/59279/, https://core.ac.uk/display/41879427, http://www.gr21.org/GR21FullAbstractBooklet.pdf#section*.177, https://dx.doi.org/10.17605/OSF.IO/ZCHTP, https://dumas.ccsd.cnrs.fr/TDS-MACS/hal-01476703 , https://indico.cern.ch/event/344173/contribution/1740565/attachments/1140145/1726912/R.A.Zahedi--Forces.of.Nature.Laws-Jan.2015-signed.pdf , http://mts-ncomms.nature.com/ncomms_files/2017/07/10/00136200/00/136200_0_art_file_2445656_bsw9c4.pdf .

Gamma titanium aluminide (γ-TiAl) based alloys are very interesting materials for structural applications at elevated temperatures owing to the combination of high specific strength, good oxidation, creep and fatigue resistance. However, their relatively poor ductility and fracture toughness remain important obstacles for their industrial applications in engineering components. The mechanical properties of these alloys, namely the poor room temperature ductility, as well as the yield stress, creep and fracture resistance, can be significantly improved by controlling the microstructure. These alloys undergo solid state phase transformations upon cooling from the high temperature α phase, which results in formation of different types of microstructures depending on the cooling conditions and the alloy composition. Low and moderate cooling rates lead to the precipitation of the γ phase as parallel plates within the α matrix, followed by the α → α2 ordering reaction. As a result of this transformation, the characteristic lamellar structure of TiAl is produced. Rapid cooling leads to a massive transformation from α to γ. It has been shown that the massive microstructure can be used as a precursor to obtain a refined microstructure through a subsequent tempering in the α + γ phase field. However, obtaining a fully massive microstructure at all depths in a component can be difficult to achieve. Numerical simulation of microstructure formation can be used as a tool to anticipate the microstructure distribution in a component depending on the local chemistry and cooling conditions. The main objective of this work was to develop microstructure models describing the formation of the massive and lamellar microstructures. Two distinct modeling approaches have been used. The first approach is a deterministic model having for objective to predict the microstructure distribution in a cast and heat treated component. The model was designed to be integrated into a FEM or FDM heat flow solver in order to calculate microstructural quantities such as the proportion of phases and lamellar spacings at each nodal point of the mesh. The modeling approach is based on a combination of nucleation and growth laws which take into account the specific mechanisms of each phase transformation. Nucleation of massive and lamellar γ is described with classical nucleation theory, accounting for the fact that nuclei are formed predominantly at α/α grain boundaries. Growth of the massive γ grains is based on theory for interface-controlled reactions. A modified Zener model is used to calculate the thickening rate of the lamellar γ precipitates. The model incorporates the effect of particle impingement and coverage of the nucleation sites by the growing phases. The driving pressures of the phase transformations are obtained from Thermo-Calc based on the actual temperature and matrix composition. The deterministic approach was used to calculate CCT diagrams and lamellar spacings, which showed to be in good agreement with experimental data obtained from dedicated heat treatment experiments and from the literature. The model permitted investigating the influence of cooling rate, alloy chemistry and average α grain size upon the amount of massive γ and the average thickness and spacing of the lamellae. In particular, it indicated that the Al depletion of the α phase during lamellar precipitation seems to play an important role in the suppression of the massive transformation at moderate cooling rate and in the large lamellar spacings observed at low cooling rate. It was also found that Nb additions enhance the formation of massive γ by lowering the lamellar transformation temperature, increasing the T0 temperature and slowing down the lamellar growth due to the low diffusivity of Nb. The second approach was a cellular automaton (CA) model, which was used to describe the formation of the lamellar microstructure on the scale of a few microns. The objective of this approach was to make a direct description of the growth of a precipitate by an interfacial ledge mechanism, and then to evaluate whether the simplifications made in the deterministic model regarding this mechanism are appropriate. The modelling approach is based on the resolution of the diffusion equations in the α and γ phases. The ledge structure at the α/γ interface is described by introducing different types of cells in the CA grid. The CA model was used to describe the formation of the lamellar microstructure at isothermal conditions and at various cooling rates. The thickening kinetics of the lamellae, the lamellar spacings and the overall kinetics of the transformation were calculated with this approach and were compared with the results obtained with the deterministic model. It was found first that the thickening kinetics of the lamellae calculated with the deterministic model are slower than those calculated with the CA model. It was established that the method used in the deterministic model to account for the growth by a ledge mechanism always leads to a growth exponent of 0.5, which is characteristic of diffusion controlled reactions. In contrast, the CA approach yields growth exponents that are comprised between 0.5 and 1, as it is expected for growth in a mixed diffusion/interface controlled mode. The incorporation of a kinetic undercooling term into the deterministic model was found to be an appropriate correction. A good agreement was finally obtained in terms of lamellar spacings and overall kinetics. The underestimated thickening kinetics of the deterministic model was observed to be partly compensated by a higher nucleation rate at the beginning of the transformation. The overall kinetics of the lamellar transformation calculated with the deterministic model was found to be sufficiently accurate to correctly predict the competition between the lamellar and massive microstructures.

This thesis addresses the computational notion of aesthetics in the framework of multistate two-dimensional cellular automata (2D CA). The measure of complexity is a core concept in computational approaches to aesthetics. Shannon's information theory provided an objective measure of complexity, which led to the emergence of various informational theories of aesthetics. However, entropy fails to take into account the spatial characteristics of 2D patterns; these characteristics are fundamental in addressing the aesthetic problem, in general, and of CA-generated patterns, in particular. This thesis proposes two empirically evaluated alternative measures of complexity, taking into account the spatial characteristics of 2D patterns and experimental studies on human aesthetic perception in the visual domain. The measures are extended to robustly quantify the complexity of multi-state 2D CA-generated patterns. The first model, spatial complexity, is based on the probabilistic spatial distribution of homogeneous/heterogeneous neighbouring cells over the lattice of a multi-state 2D cellular automaton. The second model is based on algorithmic information theory (Kolmogorov complexity) which is extended to estimate the complexity of 2D patterns. The spatial complexity measure presents performance advantage over information-theoretic models, specifically in discriminating symmetries and the orientation in CA-generated patterns, enabling more accurate measurement of complexity in relation to aesthetic evaluations of 2D patterns. A series of experimental stimuli with various structural characteristics and levels of complexity were generated by seeding 3-state 2D CA with different initial configurations for psychological experiments. The results of experimentation demonstrate the presence of correlation between spatial complexity measures and aesthetic judgements of experimental stimuli. The same results were obtained for the estimations of Kolmogorov complexity of experimental stimuli.

As an attempt to introduce interactive, content‐dependent adaptive (ICDA) image processing, a simple but powerful active image sensing and two image enhancement methods are introduced via adaptive CNN‐UM sensor‐computers. Thus, the method ICDA can be used for adaptive control of image sensing and for subsequent on‐line or off‐line image enhancement as well. The algorithms use both intensity and contrast content. The image sensing technology can be realized with the current CNN‐UM chip. Our first image enhancement method is also executable on this chip, but it is more suitable for the adaptive cellular neural network universal machine (ACNN‐UM) architecture. Some results of simulator and chip experiments and an adaptive extended cell are presented. Our second, dynamical image enhancement method is planned to be executable on a multi‐layer, complex cell CNN architecture. In (Proceedings of the 6th IEEE International Workshop on Cellular Neural Networks and their Applications(CNNA‐2000) Catania, 2000; 213–217) 3‐layer architecture is described which is capable of realizing the main part of the second enhancement method.The main issues of our paper are as follows: the novel outlook of the ICDA framework, three new methods for two key application areas of CNN‐UM, the notion of ‘regional’ adaptive computing, the novelty of application of equilibrium‐computing in the third method. However, the key novelty of our work is not just a new method and a new realization: by combining sensing and computing, dynamically and pixelwise, a new quality becomes practical. Copyright © 2002 John Wiley & Sons, Ltd.

An image recognition technology based on neural network processing of static and dynamic imagesusing cellular automata (CA) is proposed. The principles of the implementation of image processing operations withthe use of KA (in particular, pre-processing operations, contour selection and recognition) are considered. The peculiaritiesof the construction of convolutional neural networks (CNN) and their use for image recognition are analyzed.The possibilities of integrating CA into a neural network model of image classification and recognition are considered.The results of the development and software implementation of the neural network technology of image recognitionusing SNM and CA are given. Modeling of the proposed technology was carried out (on the example of the problem ofrecognizing visual objects for users with limited visual perception). development and software implementation of neuralnetwork technology for image recognition using SNM and CA.

In recent years, as the VLSI process scale had been developed into deep sub-micro dimension, the problem of the routing delay becomes critical. Especially for reconfigurable logic devices (RLDs) like Field Programmable Gate Arrays (FPGAs), the routing resources occupy approximately 90% of area and delay performance. In this paper, we propose a novel 3D routing architecture based on building 3D connections on the input and output pins of Logic Block to improve routing performance of reconfigurable logic devices (RLDs). The evaluation shows that the novel RLD with two layers of 3D routing architecture reduced 51.11% on borad area than conventional 2D 4-LUT island style FPGA.

A spin–orbit torque-based logic device that combines the Dzyaloshinskii–Moriya interaction and asymmetric shape anisotropy is proposed in this paper. Through magnetization switching of the magnetic tunnel junction (MTJ), four distinct input cases are determined to perform all of the 16 basic Boolean functions by a single magnetic tunnel junction (MTJ). With various combinations of the tilted angle of the MTJ and the value of current density, a single MTJ can serve as a reconfigurable logic gate.

A comparative study between two types of tool wear monitoring systems for milling processes is introduced in this paper. The suggested sensory fusion approach includes the implementation of an infrared camera, in addition to force, vibration, sound and acoustic emission sensors. The majority of the research work available in literature and industry focuses on using one dimensional signals, such as force, vibration. Two dimensional data, such as infrared and visual images, are limited in literature in relation to machining operations. This work compares between one dimensional and two dimensional data for the development of a tool condition monitoring system for milling processes. The paper presents a comparative study between the performance of signal and image processing algorithms using neural networks. Fourier Transformation and Wavelets analysis are used to process one dimensional and Two dimensional data respectively. The results indicate that two dimensional data obtained from infrared images has significant capability in comparison to one dimensional data for the detection of tool wear for the selected image and signal processing algorithms.

Mission-critical systems require increasing capability for fault handling and self-adaptation as their system complexities and inter-dependencies increase. Organic Computing (OC) architectures utilize biologically-inspired self-x properties which include self-configuration, self-reorganization, and self-healing which comprise the focus of this paper. To provide OC architectures with sufficient capability for exhibiting self-adaptive behavior, reconfigurable logic devices offer a suitable hardware platform. SRAM-based Field Programmable Gate Array (FPGA) logic devices can realize self-adaptation within their reconfigurable logic fabric using Evolvable Hardware techniques based on crossover, mutation, and iterative selection with intrinsic fitness assessment of the underlying hardware resources. In this paper, a dual-layer Organic Computing architecture called the Organic Embedded System (OES) is prototyped on a Xilinx FPGA reconfigurable fabric and assessed for maintainability metrics of completeness of repair, repair time, and degraded throughput during the repair phase. The approach used extends a widely known generic OC platform consisting of two layers: the Functional Layer and the Autonomic Layer. The Autonomic layer contains Autonomic Elements (AEs) that are responsible for correct operation of the corresponding Functional Elements (FEs) present on the Functional Layer. Innovations include autonomously degraded online throughput during regeneration, spare configuration aging and outlier driven repair assessment, and a uniform design for AEs despite the fact that they monitor different types of FEs. Using the OES approach; a malfunctioning or faulty AE among the population can be distinguished by its discrepant performance. The OES approach is implemented using high-level Hardware Description Language (HDL) which directs a Supervisor Element (SE) to function as a fault management unit through the collection of AE information. Experimental results show that the OES Autonomic Layer demonstrates 100% faulty component isolation for both FEs and AEs with randomly injected single faults. Using logic circuits from the MCNC-91 benchmark test set, throughput during repair phases averaged 75.05%, 82.21%, and 65.21% for the z4ml (2-bit adder), cm85a (high fan-in combinational logic), and cm138a (balanced I/O combinational logic) circuits respectively under stated conditions.

Deep Neural Networks (DNNs) have been recently in the focus of interest owing to their high-quality results in various application domains of big data analytics. The lack of end-to-end toolchains that can automatically optimize and translate a DNN algorithm, usually implemented in high-level languages, on FPGA-based platform hinders software developers to exploit such platforms. A few available toolchains, namely Xilinx DPU, have been optimized for computational expensive applications, e.g., real-time image and video processing, and therefore they introduce large performance overhead on lightweight applications, e.g., sensor-driven decision analytics. In this paper, we introduce the FLight framework to fully automatize the acceleration of lightweight DNN algorithms on FPGA-based embedded platforms. The framework takes a trained model in TensorFlow/Keras as input and generates an optimized synthesizable C++ version and maps it on the target platform using HLS tools. FLight is an easy-to-use framework and does not require deep knowledge of embedded system design since it automatically performs all the required mapping steps. FLight supports the acceleration of various deep learning algorithms, e.g., Feed-Forward Neural networks (FFNNs), Convolutional Neural Networks (CNNs), and sequential models like Recurrent Neural Networks (RNNs). We evaluated the applicability of FLight with various DNN models on both academic and industrial datasets. As a case study, we exploited the FLight framework for wind turbine condition monitoring using an industrial dataset provided by Weidmüller Monitoring Systems GmbH. The experimental results revealed the 40 times speedup compared to the Xilinx DPU framework.