Design Of Complex Architectures Research Articles

Structural alterations on copper cages containing hydrides coordinated to the central Pd(0)

A crucial component of nanocluster (NC) chemistry is the design and control of complex molecular architectures with atomic precision. Herein, we report the synthesis and structure of [PdH2Cu11{S2P(OiPr)2}6(CCPh)3] (PdH2Cu11), confirmed by single-crystal X-ray diffraction (SC-XRD), NMR spectroscopy, elemental analysis, and electrospray ionization mass spectrometry (ESI-MS). PdH2Cu11 is obtained from the reaction of a parent cluster [PdH2Cu14{S2P(OiPr)2}6(CCPh)6] (PdH2Cu14) with trifluoroacetic acid (TFA). Importantly the addition of TFA does not result in the loss of the hydride atoms located at the kernel, and H-Pd-H motif is retained. The molecular structure of PdH2Cu11 consists of a distorted 3,3,4,4,4-pentacapped trigonal prism Cu11 cage encapsulating a quasi-linear H-Pd-H unit, stabilized by dithiophosphate and alkynyl ligands. DFT calculations suggest an ionic inclusion complex of the type [{PdH2}2−]@[{Cu11{S2P(OiPr)2}6(CCPh)3}2+] and confirm the hydride positions. A non-covalent inter-cluster interaction results in the unique formation of a unique honeycomb crystal lattice. Overall, this work broadens and deepens the understanding of Pd-Cu hydride alloy nanoclusters.

Predisposition in Dynamic Covalent Chemistry: The Role of Non-Covalent Interactions in the Assembly of Tetrahedral Boronate Cages.

Directional bonding strategies guide the design of complex molecular architectures, yet challenges arise due to emergent behavior. Rigid structures face geometric constraints and sensitivity to mismatches, hindering the efficient assembly of molecular organic cages (MOCs). Harnessing intramolecular non-covalent interactions offers a promising solution, broadening geometrical possibilities and enhancing adaptability to boost assembly yields. However, identifying these interactions remains challenging, with their full potential sometimes latent until final assembly. This study explores these challenges by synthesizing boronic acid tripods with varied oxygen positions at the tripodal feet and investigating their role in assembling tetrahedral boronate MOCs. Our results reveal substantial differences in the assembly efficiency among tripods. While the building blocks with oxygen in the benzylic position relative to the central aromatic ring form the MOCs in high yields, those with the oxygen atom directly bound to the central aromatic ring, only yield traces. Through X-ray crystallography and DFT analyses, we elucidate how intramolecular interactions profoundly influence the geometry of the building blocks and cages in a relay-like fashion, highlighting the importance of considering intramolecular interactions in the rational design of (supra)molecular architectures.

Data-driven Lie point symmetry detection for continuous dynamical systems

Symmetry detection, the task of discovering the underlying symmetries of a given dataset, has been gaining popularity in the machine learning community, particularly in science and engineering applications. Most previous works focus on detecting ‘canonical’ symmetries such as translation, scaling, and rotation, and cast the task as a modeling problem involving complex inductive biases and architecture design of neural networks. We challenge these assumptions and propose that instead of constructing biases, we can learn to detect symmetries from raw data without prior knowledge. The approach presented in this paper provides a flexible way to scale up the detection procedure to non-canonical symmetries, and has the potential to detect both known and unknown symmetries alike. Concretely, we focus on predicting the generators of Lie point symmetries of partial differential equations, more specifically, evolutionary equations for ease of data generation. Our results demonstrate that well-established neural network architectures are capable of recognizing symmetry generators, even in unseen dynamical systems. These findings have the potential to make non-canonical symmetries more accessible to applications, including model selection, sparse identification, and data interpretability.

Exploration of complex nanostructures in block copolymers

The exploration of nanoscale morphologies with three-dimensional spatial arrangements in block copolymers involves the manipulation of compositional fluctuations at interfaces, induction of conformational asymmetry, and design of complex architectures. Despite the abundance of triply periodic minimal surfaces identified to date, the experimental demonstration of thermodynamically stable complex nanostructures with high-packing frustration remains limited. This research update focuses on the importance of molecular interactions for stabilizing complex nanostructures and proposes the use of end-group chemistry as a versatile method for realizing thermodynamically stable network structures with high-packing frustration in simple linear diblock copolymers. The proposed approach substantially alters phase diagrams and reveals unprecedented network structures in block copolymers. Ongoing research has the potential to generate even more diverse and stable nanostructures, thereby advancing nanotechnology and expanding our understanding of polymers and materials science. Published by the American Physical Society 2024

The Development of a Modular Adaptive Facade System Based on Pre - Calculated Light Values Through Parametric Models and Real - Time Light Sensors

The design of exhibitions often faces limitations in utilizing natural light inside the building due to the diverse and rotating nature of exhibited artworks. These objects have varying light sensitivity and prolonged exposure to excessive light can cause damage or deterioration. Therefore, in exhibition design, it is crucial to consider the direction and quantity of light that falls on the displayed objects to protect them from potential harm caused by natural light and extend their longevity. Consequently, the factor of utilizing natural light within exhibition spaces becomes a challenging element to control. The goal of this research is to develop an adaptable building envelope system to control the amount of natural light in response to the use of exhibition spaces. This was achieved by creating a parametric model using Rhinoceros software and its Grasshopper plugin for designing the building envelope. Together with the use of physical computing to create a hardware system that was programmed to develop a prototype of an adaptable building envelope we have developed a process able to optimize lighting delivery for exhibited objects. The building envelope has adjustable openings that correspond to the position of the exhibited objects and the sun. The building envelope can serve as a sunshade to prevent direct sunlight from impacting the exhibited objects while allowing the utilization of natural light in other areas within the exhibition space. The results of this research demonstrate suitable building facade designs that can be applied to exhibition spaces in various projects. The researchers evaluated the performance of the model by simulating exhibition spaces equipped with the building facade system in the southern and western directions and measured the intensity of light entering the exhibition areas. Results showed an average light intensity for the spaces ranging from 30 to 90 lux, which does not cause any damage to the exhibited objects. The researchers also tested the functionality of the building facade system with natural light and found that the control system and mechanisms worked accurately, reducing the sunlight intensity by 97.3%. This adaptable building facade system can address complex architectural design challenges and allow architects to control the amount of natural light within exhibition spaces. The design flexibility of the facade envelope system allows it to respond to different daylight periods that vary seasonally and would impact the exhibited objects.

Planar-thick panels and 3D-printed gap fillers: A hybrid digital fabrication approach to curved surface approximations

Curved surfaces may be approximated using polygonal panels to simplify complex architectural designs, yet their constructions can be expensive due to single-use moulds producing unique panels. To reduce costs, strategies like planarizing and reducing the number of different panels have been suggested, which are increasingly achievable with advancing digital fabrication technologies. However, current subtractive and additive manufacturing techniques face challenges in producing complex 3D shapes and large volumes, respectively. Combining these two techniques has been attempted, mainly with small 3D-printed nodes, leaving the direct realization of curved surface approximations unexplored. This paper extends the thick-panel origami theory to introduce planar-thick panels and 3D-printed gap fillers for cost-effective constructions. It presents a computational workflow to transform meshes into non-intersecting panels while preserving edge connectivity and frame-like gap fillers with minimized volumes. Physical prototypes demonstrate significant cost savings compared to direct 3D printing. All prototypes are accurate within a 10 mm material thickness due to the guidance of the gap fillers. The finite element analysis shows that structural performance is greatly influenced by the material properties, especially of panels, due to their relatively larger volume. Numerical examples are also presented using the k-means clustering-optimization method to reduce the number of different panels.

A Novel Perspective in Construction: Analysing the Effects of 3-D Printing Innovation

The sector of production has witnessed a transformative shift with the combination of 3-D printing technologies, providing novel strategies to design, production, and constructing practices. This investigation explores three primary 3D printing techniques: Extrusion-type techniques, Powder Bonding, and Spray Methodologies, each contributing uniquely to the sustainable development of the construction industry. We delve into the usage of revolutionary materials, including gelatin-based composites and geopolymers, and verify their effect on performance, sustainability, and structural integrity. The research underscores the importance of each approach's technique to diminishing material waste, improving durability, and facilitating complex architectural designs. Although by analysing latest improvements and experimental findings, the study gives insights into the evolving perspectives of construction methodologies, highlighting the potential for advanced environmental compatibility, cost-effectiveness, and architectural flexibility.Construction industry, 3D printing, extrusion, spray, powder bonding, environmentally friendly.

Optimization method of building energy efficiency design based on decomposition multi objective and agent assisted model

In today's global climate crisis, energy-efficient building design is crucial for achieving energy efficiency, environmental sustainability, and resident well-being. However, traditional architectural design is difficult to solve complex multi-objective and multivariate optimization problems. To overcome these challenges, this study proposes a solution: a strategy that combines decomposed multi-objective and agent assisted modeling. The proposed method decomposes complex architectural design problems into multiple manageable sub problems, with each sub problem optimized for a specific design objective. This method effectively simplifies the problem structure, allowing each subproblem to explore its solution space more focused and in-depth. Meanwhile, by combining proxy assisted modeling and utilizing proxy models to approximate actual physical processes or performance evaluations, the computational cost is reduced and the optimization process is accelerated. This study indicates that the improved multi-objective backbone particle swarm optimization algorithm relies on adaptive perturbation factors, with an average measured super volume of 29311 for one bedroom buildings and 49504 for three bedroom buildings. For the same building type, the average volume measurements of the multi-objective particle swarm optimization algorithm assisted by the decomposed surrogate model are 21153 and 40230, respectively. The proposed method effectively addresses complex multi-objective optimization problems in the field of building energy efficiency design, simplifies the problem structure, reduces computational costs through surrogate models, accelerates the optimization process, improves energy efficiency, and can support building construction to better cope with the challenges of climate change.

Alginate-CaCO3 hybrid colloidal hydrogel with tunable physicochemical properties for cell growth

Biomaterials composed of food polysaccharides are of great interest for future biomedical applications due to their great biocompatibility, tunable mechanical properties, and complex architectural designs that play a crucial role in the modulation of cell adhesion and proliferation. In this work, a facile approach was designed to obtain novel 3D alginate-CaCO3 hybrid hydrogel particles in situ. Controlling the gel concentration from 3 to 20 mg·mL−1 allows us to control the alginate-CaCO3 hydrogel particles' size and density (size variation from 1.86 to 2.34 mm and density from 1.22 to 1.29 mg/mm3). This variable also has a considerable influence on the mineralization process resulting in CaCO3 particles with varied sizes and amounts within the hydrogel beads. The measurements of Young's modulus showed that the inclusion of CaCO3 particles into the alginate hydrogel improved its mechanical properties, and Young's modulus of these hybrid hydrogel particles had a linear relationship with alginate content and hydrogel particle size. Cell experiments indicated that alginate-CaCO3 hybrid hydrogel particles can support osteoblastic cell proliferation and growth. In particular, the amount of hydroxyapatite deposition on the cell membrane significantly increased after the treatment of cells with hybrid hydrogel particles, up to 20-fold. This work offers a strategy for constructing inorganic particle-doped polysaccharide hybrid hydrogel scaffolds that provide the potential to support cell growth.

Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete

In recent years, the construction industry has been striving to make production faster and handle more complex architectural designs. Waste reduction, geometric freedom, lower construction costs, and speedy construction make the 3D-printed fiber-reinforced concrete (3DPFRC) alternative for future construction. However, achieving the optimum mixture composition for 3DPFRC remains a daunting task, entailing the consideration of multiple variables and necessitating an extensive trial-and-error experimental process. Therefore, this study investigated the application of different metaheuristic optimization algorithms to predict the compressive strength (CS) of 3DPFRC. A database of 299 data samples with 16 different input features was compiled from the experimental studies in the literature. Six metaheuristic algorithms, such as human felicity algorithm (HFA), differential evolution algorithm (DEA), nuclear reaction optimization (NRO), Harris hawks optimization (HHO), lightning search algorithm (LSA), and tunicate swarm algorithm (TSA) were applied to identify the optimal hyperparameter combination for the random forest (RF) model in predicting the CS of 3DPFRC. Different statistical metrics and 10-fold cross-validation were used to evaluate the accuracy of the models. The TSA-RF model exhibited superior performance compared to other models, achieving correlation (R), mean absolute error (MAE), and root mean square error (RMSE) values of 0.99, 2.10 MPa, and 3.59 MPa, respectively. The LSA-RF model also performed well, with R, MAE, and RMSE values of 0.99, 2.93 MPa, and 6.23 MPa, respectively. SHapley Additive exPlanation (SHAP) interpretability elucidates the intricate relationships between features and their effects on the CS, thereby offering invaluable insights for the performance-based mix proportion design of 3DPFRC.

Model-based design and multidisciplinary optimization of complex system architectures in the aircraft cabin

The aviation industry is currently facing major challenges due to environmental and socio-economic trends toward sustainable and digitalized aviation. Revolutionary, more powerful and efficient technologies must be rapidly integrated into aircraft, while aircraft manufacturers must demonstrate the required safety. To support the implementation of new concepts, the DLR Institute of System Architectures in Aeronautics is researching methods for end-to-end digitalization from the preliminary design phase to assembly and production. In this context, Model-Based Systems Engineering (MBSE) and Multidisciplinary Design Optimization are important approaches for the development of complex systems. This paper presents a method for the end-to-end use of digital models for multidisciplinary optimization of system architectures. The Systems Modeling Language (SysML) is used to represent the system architecture. The focus is on the cabin and cabin systems, since they are highly coupled to other aircraft systems and have dynamic, customer-specific configuration requirements. The system architecture in SysML is instantiated and configured by the interface to the aircraft fuselage and cabin design parameter sets in the Common Parametric Configuration Schema. The subsequent coupling of the generated system architecture model with the cabin system design model developed in Matlab allows a multidisciplinary optimization of the system properties. A sensitivity analysis is performed using the Passenger Service Unit as an example. The effects of different cabin configurations on the system architecture are investigated and interdisciplinary synergies are identified and analyzed. The results of this analysis are discussed in this paper.

Cooperative assemblies featuring hydrogen bonding and C[sbnd]H…π interactions in 2-(methanesulfonamido)benzohydrazide derivatives: Experimental, computational and biochemical assessment

The significant involvement of noncovalent interactions in catalyst design and synthesis of materials provides a fundamental impetus to explore various synthons that produce complex frameworks. Therefore, in the present work, cooperative assemblies of two methanesulfonamide containing benzohydrazide derivatives (6 and 7) were designed and analyzed. These compounds were synthesized through a straightforward approach using hydrazide 3 and 4-methyl-2-acetylthiophene (4) and 4-methoxyacetophenone (5) in refluxing ethanol. Both compounds were characterized by FTIR, NMR, LCMS and X-ray crystallography. Noncovalent interactions such as CH…π interactions and hydrogen bonding played a crucial and dominant role in the structural design of complex molecular architectures. X-ray crystal analysis reveals the presence of an intramolecular classical NH…O strong hydrogen bond and a weaker CH…O interaction in compounds 6 and 7. The NH…O arrangement of hydrazone-hydrazone cores also facilitates a CH…π close contact between the sulfonamide methyl group and the proximal benzoyl aromatic ring. Moreover, the methoxy group in 7 is also involved in CH…O/S and CH…π interactions. Hirshfeld surface analysis further revealed the involvement of these noncovalent contacts in the stabilization of molecular topology of both compounds. The energetic features of noncovalent interactions were characterized by DFT calculations while molecular electrostatic potential (MEP), QTAIM and NCIPlot computational tools rationalized the structural assemblies found in the crystal packing of both compounds. Furthermore, the biological assessment against urease enzyme revealed 13-fold superior inhibition with an IC50 value of 1.67 ± 0.19 µM for compound 6. In vitro inhibitory data was complemented with an in silico modeling where the observed interactions between the active site amino acids and compound 6 underscored the importance of heteroaryl (thienyl) component for strong inhibitory effects. Finally, the assessment of pharmacokinetic parameters proposed compound 6 as a suitable druggable inhibitor for further evaluation.

Automatic Generation of Spatial Accelerator for Tensor Algebra

Tensor algebra finds applications in various domains including machine learning applications, data analytics and others. Spatial hardware accelerators are widely used to boost the performance of tensor algebra applications. It has a complex hardware architecture and rich design space. Prior approaches based on manual implementation lead to low programming productivity, making it hard to explore the large design space. In this paper, we propose Tensorlib, a framework for generating spatial hardware accelerators for tensor algebra applications. Tensorlib is motivated by the observation that, tensor dataflows can be expressed with linear transformations, and they share common hardware modules which can be reused across different designs. Tensorlib first uses Space-Time Transformation to explore different dataflows, which can compactly represent the hardware dataflow using a transformation matrix. Next, we identify the common structures of different dataflows and build parameterized hardware module templates. Our generation framework can select the needed hardware modules for each dataflow, connect the modules using a specified interconnection pattern, and automatically generate the complete hardware accelerator design. Tensorlib remarkably improves the productivity for the development and optimization of spatial hardware architecture, providing a rich design space with trade-offs in performance, area, and power. Experiments show that Tensorlib can automatically generate hardware designs with different dataflows for a variety of tensor algebra programs. Tensorlib can achieve 318 MHz frequency and 786 GFLOP/s throughput for matrix multiplication kernel on Xilinx VU9P FPGA, which outperforms the state-of-the-art generators.

Additive manufacturing of silicone-thermoplastic elastomeric composite architectures

Additive manufacturing (AM) methods such as fused filament fabrication (FFF) and direct ink writing (DIW) enable the free-form design and printing of complex architectures using a range of materials. Compared with traditional manufacturing technologies, AM methods are highly automated to reduce waste, assembly costs, and processing errors. By combining different AM deposition methods, we can fabricate composite materials with desired spatial arrangements and anisotropies to meet unique design requirements and to produce integrated multi-material components that are not easily manufactured by traditional strategies. To address a wide variety of mechanical performance requirements, developing hybrid AM technologies into a low-cost, fully-automated manufacturing process can realize a number of new types of elastomer composite architectures. In this work, FFF and DIW methods have been combined to fabricate thermoplastic polyurethane (TPU) reinforced silicone composite materials. Several specimens with different silicone and TPU infill patterns have been designed to characterize the mechanical compression responses of these structures. With 40–80% infill percentages of silicone and 20–60% infill percentages of TPU, composites specimens can be designed to get any specified compression response between 600 N and 9000 N. This TPU-reinforced silicone printing technique can be applied for silicone composite architecture design in a wide range of applications that require tunable elastic responses.

Oceanic mesoscale eddy detection and convolutional neural network complexity

Deep learning has drawn the attention of oceanographic researchers over the past few years, making the research community adopt computer vision techniques for oceanic mesoscale eddy detection on satellite altimetry gridded products. In this paper, we describe a convolutional neural network designed to detect eddies in satellite altimetry maps after being trained using segmentation masks provided by the OpenEddy detection algorithm. Against the current trend, in which increasingly complex neural networks are being proposed to address this problem, our design is relatively simple and yet provides competitive performance when compared to any of the previous deep learning methods reported in the literature. Furthermore, we show that our model is less sensitive to timely variations than the traditional models based on physical and geometric features defined by human experts, making it possible for our model to use the general data context to identify eddies that those traditional models would have missed. These results prove that overly complex neural network architectural designs are not required to solve the eddy detection problem on altimetry maps and generate a sufficiently good model for most practical applications in the field of marine sciences. • Increasingly complex CNN architectures are being proposed for eddy detection. • We prove that excessively complex CNN designs are not needed to solve this problem. • Our proposal improves state-of-the-art designs and requires to train much less parameters. • Our data augmentation method improves performance without artificial transformations. • Our model is less sensitive to timely variations and detects eddies that traditional models would miss.

A Study of the Morphological Design of Architecture from a Geometric Logic Perspective

In order to further solve the problem of the application and auxiliary function of geometric prototypes in the process of complex architectural structural form design, a method of architectural form design research based on the perspective of geometric logic is proposed. Taking buildings as an example, combined with the requirements of architectural form design, the optimal design of architectural structure form is studied with the help of genetic algorithm, focusing on the verification of building plane and space optimization design under displacement constraints. The results show that after 3257 iterations, the adaptive degree does not change, the corresponding maximum uniqueness is 0.1196 mm, and the weight of the truss structure is 0.3585 t, indicating that the structural design based on geometric logic and genetic algorithm can effectively satisfy the optimal form design of the building form under the constraints. The design method of geometric logic incorporates the whole process of building use, building site, building system, and production and construction into the control scope of the design. In the design process, according to the real environment, through the translation and logic construction of computer language simulation, the architectural concept scheme is generated, and finally the building product is promoted. Complete with high precision.

Brief Analysis of the Development Trend of Commercial Complex Architectural Design

Brief Analysis of the Development Trend of Commercial Complex Architectural Design

Timber structures made of naturally curved oak wood: prototypes and processes

Highly optimised processing workflows characterise today’s wood industry. The gained efficiency is mainly directed towards making standardised linear materials that fulfil the market’s expectation of a continuous flow of identical products with certain constant specifications. The research presented here seeks to question these limitations and provide another approach to the use of wood in construction. The study involves complex geometry handling, architectural design, and material and structural considerations. Trees absorb CO2 during growth, and as a construction material, wood can function as CO2 storage, thereby reducing the levels in the atmosphere during the lifetime of the building—and even longer if the building components can be reused. We have seen a significant rise in wood construction over the past few years. This is partly due to growing climate awareness and the increased availability of engineered wood products (EWP). EWPs are reliant on uniform tree production. With growing interest in using wood for construction, an increase in plantation forests is predicted, leading to a lack of biodiversity in the affected areas. The consideration for the general climate expressed through the increased use of wood as a building material may thus appear to be in contrast to the efforts to improve biodiversity. This research seeks to provide an alternative route where non-uniform wood, usually used as firewood, can be used as a construction material. The project demonstrates possibilities that emerge from engaging with the wood as a specific occurrence of a biological entity rather than a standardised material. While historical precedents inspire the research, the project has been developed using digital tools, such as laser scanning, algorithmic design and robotic fabrication. We have developed a unique design-to-production workflow that uses curved natural wood in its original form to enable curved architectural designs. The workflow thereby links the inherent properties of the wood to a distinct mode of expression. The wood is retrieved from a sawmill that collects discarded tree trunks from local forests. The sawlogs are registered with a 3D scanner, and a customised parametric method is used to handle the geometric information and establish a database of the irregular saw logs. A custom-made algorithmic design tool identifies where the sawlogs fit best in a predefined construction design based on the database. Machining data is directly extracted for the subsequent robotic processing. The irregular shape of the material suggests a discrete analysis of the structural properties of each component. A series of destructive physical tests are carried out to indicate the capacity of the structural system and the joint solution. To demonstrate the viability of the workflow, study challenges in controlling tolerances and humidity, and develop an assembly strategy, we have produced a construction prototype of 15 members in the size of 3 m in width and 4 m in height.

Hierarchical Interfaces as Fracture Propagation Traps in Natural Layered Composites.

Compared with their monolithic version, layered structures are known to be beneficial in the design of materials, especially ceramics, providing enhanced fracture toughness, mechanical strength, and overall reliability. This was proposed in recent decades and extensively studied in the engineering literature. The source of the property enhancement is the ability of layered structures to deflect and often arrest propagating cracks along internal interfaces between layers. Similar crack-stopping abilities are found in nature for a broad range of fibrillary layered biological structures. Such abilities are largely governed by complex architectural design solutions and geometries, which all appear to involve the presence of various types of internal interfaces at different structural levels. The simultaneous occurrence at several scales of different types of interfaces, designated here as hierarchical interfaces, within judiciously designed layered composite materials, is a powerful approach that constrains cracks to bifurcate and stop. This is concisely described here using selected biological examples, potentially serving as inspiration for alternative designs of engineering composites.

Hardware Implementation Study of Particle Tracking Algorithm on FPGAs

In recent years, the technological node used to implement FPGA devices has led to very high performance in terms of computational capacity and in some applications these can be much more efficient than CPUs or other programmable devices. The clock managers and the enormous versatility of communication technology through digital transceivers place FPGAs in a prime position for many applications. For example, from real-time medical image analysis to high energy physics particle trajectory recognition, where computation time can be crucial, the benefits of using frontier FPGA capabilities are even more relevant. This paper shows an example of FPGA hardware implementation, via a firmware design, of a complex analytical algorithm: The Hough transform. This is a mathematical spatial transformation used here to facilitate on-the-fly recognition of the trajectories of ionising particles as they pass through the so-called tracker apparatus within high-energy physics detectors. This is a general study to demonstrate that this technique is not only implementable via software-based systems, but can also be exploited using consumer hardware devices. In this context the latter are known as hardware accelerators. In this article in particular, the Xilinx UltraScale+ FPGA is investigated as it belongs to one of the frontier family devices on the market. These FPGAs make it possible to reach high-speed clock frequencies at the expense of acceptable energy consumption thanks to the 14 nm technological node used by the vendor. These devices feature a huge number of gates, high-bandwidth memories, transceivers and other high-performance electronics in a single chip, enabling the design of large, complex and scalable architectures. In particular the Xilinx Alveo U250 has been investigated. A target frequency of 250 MHz and a total latency of 30 clock periods have been achieved using only the 17 ÷ 53% of LUTs, the 8 ÷ 12% of DSPs, the 1 ÷ 3% of Block Rams and a Flip Flop occupancy range of 9 ÷ 28%.