Architectural Constraints Research Articles

NISQ circuit compilation is the travelling salesman problem on a torus

Noisy, intermediate-scale quantum (NISQ) computers are expected to execute quantum circuits of up to a few hundred qubits. The circuits have to conform to NISQ architectural constraints regarding qubit allocation and the execution of multi-qubit gates. Quantum circuit compilation (QCC) takes a nonconforming circuit and outputs a compatible circuit. Can classical optimisation methods be used for QCC? Compilation is a known combinatorial problem shown to be solvable by two types of operations: (1) qubit allocation, and (2) gate scheduling. We show informally that the two operations form a discrete ring. The search landscape of QCC is a two dimensional discrete torus where vertices represent configurations of how circuit qubits are allocated to NISQ registers. Torus edges are weighted by the cost of scheduling circuit gates. The novelty of our approach uses the fact that a circuit’s gate list is circular: compilation can start from any gate as long as all the gates will be processed, and the compiled circuit has the correct gate order. Our work bridges a theoretical and practical gap between classical circuit design automation and the emerging field of quantum circuit optimisation.

Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems.

Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically inconsistent results when violating fundamental constraints. Here, we introduce a systematic way of enforcing nonlinear analytic constraints in neural networks via constraints in the architecture or the loss function. Applied to convective processes for climate modeling, architectural constraints enforce conservation laws to within machine precision without degrading performance. Enforcing constraints also reduces errors in the subsets of the outputs most impacted by the constraints.

Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial

Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communications to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work.

SAVE: Security & safety by model-based systems engineering on the example of automotive industry

Cyber-physical systems (CPS), like autonomous vehicles, are intelligent and networked. The development of such systems requires interdisciplinary cooperation between different stakeholders. A lack of system understanding between stakeholders can lead to unidentified security threats & safety hazards, resulting in high costs in product development. In particular, a lack of an integrative consideration of security threats & safety hazards can compromise safety compliance for CPS. Model-based systems engineering (MBSE) improves the understanding of systems between stakeholders by additionally creating supporting models. However, MBSE approaches only partially address security threats & safety hazards. In particular, their integrative consideration is not taken into account. Established security & safety approaches either are only applicable to specific disciplines or only partially consider security threats & safety hazards. In this paper we present a method that enables the early identification of safety relevant security threats. The method is designed to be applied in workshops with an interdisciplinary team of stakeholders and is used to determine initial results for the system architecture design phase. We illustrate our approach with the example of the automotive sector. To build a realistic system architecture we identified 18 architectural vehicle components including relevant architectural constraints. Finally, we present an evaluation of the method, based on a workshop with 30 master students.

A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping

One of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. However, no comprehensive model exists that links all steps of processing from vision to action. We hypothesized that a recurrent neural network mimicking the modular structure of the anatomical circuit and trained to use visual features of objects to generate the required muscle dynamics used by primates to grasp objects would give insight into the computations of the grasping circuit. Internal activity of modular networks trained with these constraints strongly resembled neural activity recorded from the grasping circuit during grasping and paralleled the similarities between brain regions. Network activity during the different phases of the task could be explained by linear dynamics for maintaining a distributed movement plan across the network in the absence of visual stimulus and then generating the required muscle kinematics based on these initial conditions in a module-specific way. These modular models also outperformed alternative models at explaining neural data, despite the absence of neural data during training, suggesting that the inputs, outputs, and architectural constraints imposed were sufficient for recapitulating processing in the grasping circuit. Finally, targeted lesioning of modules produced deficits similar to those observed in lesion studies of the grasping circuit, providing a potential model for how brain regions may coordinate during the visually guided grasping of objects.

An assessment of damper placement methods considering upfront damper cost

Previous studies have shown that earthquake repair costs can be minimised by using large levels of supplemental damping and uniform damper placement. However, it is not always feasible to achieve this damping due to structural or financial restraints. Iterative damper placement methods may be able to achieve higher levels of damping than simple methods such as uniform damping for the same total damper cost. In this work, six damper placement methods were assessed based on structural and non-structural repair costs. The total damper cost was constrained to be the same in each case. The scope of work was limited to linear fluid viscous dampers, concentric braced frames and regular structures. The iterative methods were found to provide a greater total damping coefficient to the structures than the simple methods. This resulted in a higher supplemental damping ratio and lower repair costs. If upfront funds are limited, or if architectural constraints prevent the placement of dampers in lower storeys, then iterative methods provide the most favourable total-building seismic performance. However, the conclusions should be extended cautiously. Although iterative methods are favourable when the upfront damper investment is strictly limited, in terms of total-building seismic performance, it is advantageous to provide a large damping ratio using uniform damping.

A Numerical Investigation on the In-Plane Behavior of Perforated Unreinforced Masonry Walls

Considering architectural constraints, the existence of openings in unreinforced masonry (URM) walls is inevitable. Moreover, the observations of damaged URM buildings after previous earthquakes and the results of experimental studies on the seismic performance of masonry shear walls confirm that the in-plane behavior of perforated walls depends on the coupling of piers and spandrels. Accordingly, some improvements have been made in recent editions of international seismic retrofitting codes. This study is concerned with the in-plane behavior of perforated URM walls through a finite element (FE) study. For this purpose, 3D micromodeling is utilized for the simulation of the masonry walls in the FE software ABAQUS. At first, the FE model is verified using the results of an experimental study on large-scale perforated walls under simultaneous gravity and lateral in-plane loads. The numerical and experimental results show a good agreement with each other which confirms the reliability of the model for the simulation of URM walls. Subsequently, a parametric study is conducted by means of the validated FE model. The pier width and spandrel depth of the walls as well as the intensity of the gravity load on the piers are considered as the influential variables in the numerical investigation. Finally, the results of the numerical study are presented in terms of load–displacement curves, ultimate mode of failure, and the proportions of the internal shear force induced in piers at different story drift levels. According to the results of the current study, the geometrical properties of the walls and the gravity load have a substantial effect on the in-plane strength of the walls. Additionally, the role of spandrels in the global seismic performance of URM walls and piers is not negligible and should be considered in the seismic vulnerability assessment of existing buildings.

Runtime verification for dynamic architectures

The architecture of a system captures important design decisions for the system. Over time, changes in a system's implementation may lead to violations of specific design decisions. This problem is common in industry and known as architectural erosion. Since it may have severe consequences on the quality of a system, research has focused on the development of tools and techniques to address the presented problem. As of today, most of the approaches to detect architectural erosion employ static analysis techniques. While these techniques are well-suited for the analysis of static architectures, they reach their limit when it comes to dynamic architectures. Thus, in this paper, we propose an alternative approach based on runtime verification: We describe techniques to formally specify constraints for dynamic architectures and algorithms to translate such specifications to instrumentation code and corresponding monitors. The approach is implemented in Eclipse/EMF, demonstrated through a running example, and evaluated using two case studies.

Learning visual contour tracing in a deep recurrent network based on a cortical columnar architecture

Problem. Visual segmentation mechanisms comprise rapid base grouping of simple features by pre-configured circuits and slower but more flexible incremental grouping by dynamically enhancing feature representations (Roelfsema, Ann. Rev. Neurosci., 2006). How can neural circuits for low- and intermediate-level vision learn such mechanisms? Deep learning provides a framework to develop neuroscience-related models, in which inductive biases constrain model computations (Richards et al., Nat. Neurosci., 2019). Here, we investigate the task of contour tracing to incrementally group visual items for decision-making in a reinforcement learning scenario (Roelfsema, Lamme & Spekreijse, Nature, 1998) with architectural constraints inspired by cortical columns. Method. We propose a multi-layer, recurrent, convolutional network where each layer is composed of retinotopically organized cortical columns. Such columns are modeled as pairs of interacting excitatory-inhibitory (E-I) neurons in discrete feature channels. E-I pair activations are computed from input filtering, lateral integration, activity normalization over neuronal pools, and top-down modulating feedback. Reinforcement signals are provided during training, from which the network can learn rewarding actions over sequential stimulus presentations, similar to the corresponding psychophysical experiments in monkeys, where the task is to fixate and hold gaze at a fixation cue and finally making a saccade to target items indicated by tagging via learned neural tracing. Results and Conclusion. The network successfully solves incremental grouping tasks after learning to act in accordance with the different phases of contour target presentation. Activity spreads along the target line to enhance its neural representation relative to a distractor. Inspection of the model reveals that maintaining fixations is mediated by E-I interactions, while the tracing operation that constitutes incremental grouping is mainly implemented by feedforward and recurrent excitatory interactions.

Lighting of Museums and Art Galleries

A museum is a place where people can explore and learn the past, the present and the future of culture and science. Amongst the other technical systems, the lighting equipment must satisfy a number of requirements. For each project, lighting designers must face challenges in order to encompass the given specifications in a single lighting concept: protection of the artefacts, economic convenience, architectural constraints and, last but not least, the creative ambitions of the artist. This paper analyses the lighting environment of a real art gallery with the aim to identify a satisfactory solution in terms of enjoyment, artefact preservation and energy consumption. By means of a simulation software, suitable general and spot lighting devices are chosen to fulfil the requirements whilst avoiding damages to the artefacts, and lighting parameters are obtained to evaluate the proposed solution. The work shows that advanced LED technologies can be effectively used to modify a museum lighting environment and turn it into the classical gallery archetype.

Influence of fluid mechanics on metastasis formation

Metastases are the main cause of cancer-related deaths. The chain of events leading to their development is called "the metastatic cascade". The biological and biochemical aspects of this process have been well studied but the importance of biomechanical parameters only recently became a focus in the field. Studies have shown the biological fluids (blood, lymph and interstitial fluid) to play a key role in the metastatic cascade. These fluids participate in the transport of circulating tumor cells (CTCs) as well as the factors that they secrete, while at the same time influencing the events of the metastatic cascade through the forces that they generate. The hemodynamic properties and topological constraints of the vascular architecture control the formation of metastatic niches and the metastatic potential of tumor cells. In this review, we discuss the importance of these mechanical forces and highlight the novel questions and research avenues that they open.

Facies architecture and palaeoenvironmental constraints of subaqueous felsic volcanism in the Iberian Pyrite Belt: The Paymogo Volcano-Sedimentary Alignment

The Paymogo Volcano-Sedimentary Alignment (PVSA) is an essentially igneous sequence associated with VHMS deposits of the Iberian Pyrite Belt. Unlike other areas of this mining district, where mafic igneous rocks are abundant, in the PVSA these are essentially felsic. Two facies associations have been distinguished based on petrographic and chemical features: a lower dacite facies association and an upper rhyolite facies association. The main ore horizons appear related to volcanic breccias of the rhyolite facies association. A detailed petrographic study of these breccias suggests they are resedimented hyaloclastite deposits. The analysis of volcanic facies indicates that the volcanism developed at water depths deeper than 500 m, consistent with the minimum depth at which VHMS deposits can form. We conclude that detailed volcanological studies provide effective constraints to palaeoenvironmental interpretations, which are relevant in assessing regional stratigraphy and area selection for VHMS exploration.

Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware

Neuromorphic architectures implement biological neurons and synapses to execute machine learning algorithms with spiking neurons and bio-inspired learning algorithms. These architectures are energy efficient and therefore, suitable for cognitive information processing on resource and power-constrained environments, ones where sensor and edge nodes of internet-of-things (IoT) operate. To map a spiking neural network (SNN) to a neuromorphic architecture, prior works have proposed design-time based solutions, where the SNN is first analyzed offline using representative data and then mapped to the hardware to optimize some objective functions such as minimizing spike communication or maximizing resource utilization. In many emerging applications, machine learning models may change based on the input using some online learning rules. In online learning, new connections may form or existing connections may disappear at run-time based on input excitation. Therefore, an already mapped SNN may need to be re-mapped to the neuromorphic hardware to ensure optimal performance. Unfortunately, due to the high computation time, design-time based approaches are not suitable for remapping a machine learning model at run-time after every learning epoch. In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at run-time. Our design methodology operates in two steps – step 1 is a layer-wise greedy approach to partition SNNs into clusters of neurons and synapses incorporating the constraints of the neuromorphic architecture, and step 2 is a hill-climbing optimization algorithm that minimizes the total spikes communicated between clusters, improving energy consumption on the shared interconnect of the architecture. We conduct experiments to evaluate the feasibility of our algorithm using synthetic and realistic SNN-based applications. We demonstrate that our algorithm reduces SNN mapping time by an average 780x compared to a state-of-the-art design-time based SNN partitioning approach with only 6.25% lower solution quality.

Enhancing Explainability of Neural Networks Through Architecture Constraints.

Prediction accuracy and model explainability are the two most important objectives when developing machine learning algorithms to solve real-world problems. Neural networks are known to possess good prediction performance but suffer from a lack of model interpretability. In this article, we propose to enhance the explainability of neural networks through the following architecture constraints: 1) sparse additive subnetworks; 2) projection pursuit with orthogonality constraint; and 3) smooth function approximation. It leads to an enhanced explainable neural network (ExNN) with a superior balance between prediction performance and model interpretability. We derive sufficient identifiability conditions for the proposed ExNN model. The multiple parameters are simultaneously estimated by a modified minibatch gradient descent method based on the backpropagation algorithm for calculating the derivatives and the Cayley transform for preserving the projection orthogonality. Through simulation study under six different scenarios, we compare the proposed method to several benchmarks, including least absolute shrinkage and selection operator, support vector machine, random forest, extreme learning machine, and multilayer perceptron. It is shown that the proposed ExNN model keeps the flexibility of pursuing high prediction accuracy while attaining improved interpretability. Finally, a real data example is employed as a showcase application.

RDON: a rack-scale disaggregated data center network based on a distributed fast optical switch

Aiming to solve the low utilization and high operational cost in current data centers and the diversified resource requirements for different application types, we propose and investigate what we believe to be a novel rack-scale disaggregated optical data center network (RDON). Different types of hardware resources (i.e., CPU, memory, and storage) are deployed in standalone resource pools of the RDON. The RDON architecture encompasses nanosecond distributed fast optical switches for the low latency and high bandwidth interconnects between the computing and memory nodes, while electrical packet switches are utilized to support the traffic of latency insensitivity and low bandwidth between memory nodes and storage nodes. By replacing the onboard data bus with a network interconnection, RDON architecture can provide a fine granularity hardware resource and relieve the constraints of server-centric architecture. To assess the performance of the RDON under realistic scenarios, statistics of diverse applications are captured from the data center lab. The RDON is modeled based on the OMNeT++ discrete event simulator and compared with the server-centric architecture. Numerical results show that the performance of the RDON is closely coupled with the type of deployed application. Compared to the server-centric architecture, the RDON can achieve up to 2.44 × faster running time when running a single application, whereas at most 2.25 × is the fastest running time under coexisting diverse types of applications. Finally, the analysis of costs and power consumption show that, with respect to server-centric architecture, the RDON requires 21.5% extra capital cost but 46.7% less power consumption.

Combined compression of multiple correlated data streams for online-diagnosis systems

Online fault-diagnosis is applied to various systems to enable an automatic monitoring and, if applicable, the recovery from faults to prevent the system from failing. For a sound decision on occurred faults, typically a large amount of sensor measurements and state variables has to be gathered, analyzed and evaluated in real-time. Due to the complexity and the nature of distributed systems all this data needs to be communicated among the network, which is an expensive affair in terms of communication resources and time. In this paper we present compression strategies that utilize the fact that many of these data streams are highly correlated and can be compressed simultaneously. Experimental results show that this can lead to better compression ratios compared to an individual compression of the data streams. Moreover, the algorithms support real-time constraints for time-triggered architectures and enable the data to be transmitted by means of shorter messages, leading to a reduced communication time and improved scheduling results. With an example data set we show that, depending on the parameters of the compression algorithm, more than one third of the bits (34.3%) in the data communication can be saved while only on about 0.2% of all data values a slight loss of accuracy occurs. This means 99.8% of the data values can be correctly delivered without any loss but with a significant reduction of bandwidth demands.

Mars Sample Return Campaign Concept Status

This paper will provide an overview of current options and specific concepts for a potential Mars Sample Return (MSR) architecture being jointly studied by NASA and ESA. Overall objectives and mission options will be described, including the architecture's constraints and notional timelines. The paper will highlight architecture-level trade studies, including specific elements and their status. The overall Sample Retrieval Lander (SRL) mission concept, including vehicle options will be described, including the Mars Ascent Vehicle (MAV), Sample Fetch Rover (being studied by ESA), Orbiting Sample container (OS), and tube transfer robotics systems. The concept and status of the Earth Return Orbiter (ERO) mission, being studied by ESA, and the Capture/Containment and Return System (CCRS) which would be the payload on the ERO, will be discussed.The information provided about possible Mars sample return architectures and concepts is for planning and discussion purposes only. NASA and ESA have made no official decisions to implement Mars Sample Return.

An evolutionary design approach to shell-infill structures

Shell-infill structures with internal porous architecture are increasingly used in additive manufacturing because of their advantages such as material saving, printing efficiency, and thermal stress release. The study is built upon the bi-directional evolutionary structural optimization (BESO) method. An evolutionary design approach to shell-infill structures using flexible control of the infill architecture is proposed in this study. Specifically, the general structural configuration with a coating shell is first designed by assuming that the infill consists of a weak material. The delicate fine infill architecture is subsequently designed by extending BESO to consider the maximum length scale constraints of the infill architecture. Shell-infill designs of variant functionally graded infill architectures are additively manufactured using stereolithography and their mechanical performance are tested. Numerical and experimental results indicate that the proposed method is more effective in providing shell-infill designs with optimal stiffness and robust performance compared to conventional designs with uniform lattice infill.

The sound diffusion in Italian Opera Houses: Some examples

Soundfield diffuseness in rooms is considered a fundamental aspect of a high-quality room acoustics. Since early studies by Hodgson up to more recent studies of Shtrepi and Embrechts, it was shown that high levels of sound diffuseness could guarantee blending of music, as well as spatial sound perception by listeners, and this could enhance the global indoor acoustic quality. Conversely, Italian-style Opera houses represent an important architectural place, in which the special features of the rich decorations, and the specific characteristics of the volume, give a unique atmosphere, including a peculiar psycho-acoustics impression. However, some geometric properties of the opera houses could influence the global acoustic perception. The shape of the marmorino wall on the stalls, as well as the parallelism of the lateral walls in the boxes, often causes a lack of spaciousness and sometimes in the worst cases provokes focalization. This phenomenon leads to design special devices that could be inserted in the theatres, to avoid focalization, even if they are rarely accepted. This article deals with the design of some acoustic diffusing panels and their functioning in three different theatres, combining both acoustics needs with architectural constraints. The article starts analysing and commenting on the issues that resulted from the measurements conducted in an Italian opera house. In the following step, three examples of the design of diffusing panels are proposed. Finally, the results of diffusion and scattering coefficient of panels realized in the last theatre considered here are reported.

Structure of the Polymer Backbones in polyMOF Materials.

The molecular connectivity of polymer-metal-organic framework (polyMOF) hybrid materials was investigated using density functional theory calculations and solid-state NMR spectroscopy. The architectural constraints that dictate the formation of polyMOFs were assessed by examining poly(1,4-benzenedicarboxylic acid) (pbdc) polymers in two archetypical MOF lattices (UiO-66 and IRMOF-1). Modeling of the polyMOFs showed that in the IRMOF-1-type lattice, six, seven, and eight methylene (-CH2-) groups between 1,4-benzenedicarboxylate (terephthalate, bdc2-) units can be accommodated without significant distortions, while in the UiO-66-type lattice, an optimal spacing of seven methylene groups between bdc2- units is needed to minimize strain. Solid-state NMR supports these predictions and reveals pronounced spectral differences for the same polymer in the two polyMOF lattices. With seven methylene groups, polyUiO-66-7a shows 7 ± 3% of uncoordinated terephthalate linkers, while these are undetectable (<4%) in the corresponding polyIRMOF-1-7a. In addition, NMR-detected backbone mobility is significantly higher in the polyIRMOF-1-7a than in the corresponding polyUiO-66-7a, again indicative of taut chains in the latter.