Efficient Implementation Research Articles

Geometric Decomposition and Efficient Implementation of High Order Face and Edge Elements

Geometric Decomposition and Efficient Implementation of High Order Face and Edge Elements

High-Throughput Polar Code Decoders with Information Bottleneck Quantization

In digital baseband processing, the forward error correction (FEC) unit belongs to the most demanding components in terms of computational complexity and power consumption. Hence, efficient implementation of FEC decoders is crucial for next-generation mobile broadband standards and an ongoing research topic. Quantization has a significant impact on the decoder area, power consumption and throughput. Thus, lower bit widths are preferred for efficient implementations but degrade the error correction capability. To address this issue, a non-uniform quantization based on the Information Bottleneck (IB) method is proposed that enables a low bit width while maintaining the essential information. Many investigations on the use of the IB method for Low-density parity-check code) LDPC decoders exist and have shown its advantages from an implementation perspective. However, for polar code decoder implementations, there exists only one publication that is not based on the state-of-the-art Fast Simplified Successive-Cancellation (Fast-SSC) decoding algorithm, and only synthesis implementation results without energy estimation are shown. In contrast, our paper presents several optimized Fast-SSC polar code decoder implementations using IB-based quantization with placement and routing results using advanced 12 nm FinFET technology. Gains of up to 16% in area and 13% in energy efficiency are achieved with IB-based quantization at a Frame Error Rate (FER) of 10−7 and a polar code of N=1024,R=0.5 compared to state-of-the-art decoders.

Discrete-Time Impedance Control for Dynamic Response Regulation of Parallel Soft Robots

Accurately controlling the dynamic response and suppression of undesirable dynamics such as overshoots and vibrations is a vital requirement for soft robots operating in industrial environments. Pneumatically actuated soft robots usually undergo large overshoots and significant vibrations when deactuated because of their highly flexible bodies. These large vibrations not only decrease the reliability and accuracy of the soft robot but also introduce undesirable characteristics in the system. For example, it increases the settling time and damages the body of the soft robot, compromising its structural integrity. The dynamic behavior of the soft robots on deactuation needs to be accurately controlled to increase their utility in real-world applications. The literature on pneumatic soft robots still does not sufficiently address the issue of suppressing undesirable vibrations. To address this issue, we propose the use of impedance control to regulate the dynamic response of pneumatic soft robots since the superiority of impedance control is already established for rigid robots. The soft robots are highly nonlinear systems; therefore, we formulated a nonlinear discrete sliding mode impedance controller to control the pneumatic soft robots. The formulation of the controller in discrete-time allows efficient implementation for a high-order system model without the need for state-observers. The simplification and efficiency of the proposed controller enable fast implementation of an embedded system. Unlike other works on pneumatic soft robots, the proposed controller does not require manual tuning of the controller parameters and automatically calculates the parameters based on the impedance value. To demonstrate the efficacy of the proposed controller, we used a 6-chambered parallel soft robot as an experimental platform. We presented the comparative results with an existing state-of-the-art controller in SMC control of pneumatic soft robots. The experiment results indicate that the proposed controller can effectively limit the amplitude of the undesirable vibrations.

Alrecon: computed tomography reconstruction web application based on Solara

Synchrotron X-ray computed tomography is a non-destructive 3D imaging technique that offers the possibility to study the internal microstructure of samples with high spatial and temporal resolution. Given its unmatched image quality and acquisition speed, and the possibility to preserve the specimens, there is an increasing demand for this technique, from scientific users from innumerable disciplines. Computed tomography reconstruction is the computational process by which experimental radiographs are converted to a meaningful 3-dimensional image after the scan. The procedure involves pre-processing steps for image background and artifact correction on raw data, a reconstruction step approximating the inverse Radon-transform, and writing of the reconstructed volume image to disk. Several open-source Python packages exist to help scientists in the process of tomography reconstruction, by offering efficient implementations of reconstruction algorithms exploiting central or graphics processing unit (CPU and GPU, respectively), and by automating significant portions of the data processing pipeline. A further increase in productivity is attained by scheduling and parallelizing demanding reconstructions on high performance computing (HPC) clusters. Nevertheless, visual inspection and interactive selection of optimal reconstruction parameters remain crucial steps that are often performed in close interaction with the end-user of the data. As a result, the reconstruction task involves more than one software. Graphical user interfaces are provided to the user for fast inspection and optimization of reconstructions, while HPC resources are often accessed through scripts and command line interface. We propose Alrecon, a pure Python web application for tomographic reconstruction built using Solara. Alrecon offers users an intuitive and reactive environment for exploring data and customizing reconstruction pipelines. By leveraging upon popular 3D image visualization tools, and by providing a user-friendly interface for reconstruction scheduling on HPC resources, Alrecon guarantees productivity and efficient use of resources for any type of beamline user.

Master–slave elimination scheme for arbitrary smooth nonlinear multi-point constraints

AbstractNonlinear multi-point constraints are essential in modeling various engineering problems, for example in the context of (a) linking individual degrees of freedom of multiple nodes to model nonlinear joints, (b) coupling different element types in finite element analysis, (c) enforcing various types of rigidity in parts of the mesh and (d) considering deformation-dependent Dirichlet boundary conditions. One method for addressing constraints is the master–slave elimination, which offers the benefit of reducing the problem dimension as opposed to Lagrange multipliers and the penalty method. However, the existing master–slave elimination method is limited to linear constraints. In this paper, we introduce a new master–slave elimination method for handling arbitrary smooth nonlinear multi-point constraints in the system of equations of the discretized system. We present a rigorous mathematical derivation of the method. Within this method, new constraints can be easily considered as an item of a “constraint library”; i.e. no case-by-case-programming is required. In addition to the theoretical aspects, we also provide helpful remarks on the efficient implementation. Among others, we show that the new method results in a reduced computational complexity compared to the existing methods. The study also places emphasis on comparing the new approach with existing methods via numerical examples. We have developed innovative benchmarks which encompass all relevant computational properties, and provide analytical and reference solutions. Our findings demonstrate that our new method is as accurate, robust and flexible as the Lagrange multipliers, and more efficient due to the reduction of the total number of degrees of freedom, which is particularly advantageous when a large number of constraints have to be considered.

BARRIERS IN ENERGY EFFICIENCY IMPLEMENTATION IN INDUSTRIES

The energy consumption of the industries in U.P is around 8% among the several energy consuming sectors. The most energy consuming sector in U.P is the residential sector with 80.36% of the total energy consumption followed by the industrial and transport sector with energy consumption share of approx. 8% and 7% respectively. In the industries, electricity is the fourth most consumed energy with 13.61% of share among the coal (46.24%), diesel (15%) and fuel wood (23.83%). The Drivers & Barriers is based on insight into the consumer's choice and decision making behavior which are very important parameters in the implementation of the energy efficiency. It demonstrates that a good idea may move and inspire people, but it takes more to make them act towards the implementation of the energy efficiency. Key Words: Energy Efficiency, Diesel Generator,Energy Audit

A computational two-scale approach to cancellous bone remodelling

We propose a novel two-scale (meso-macro-scale) approach to computationally capture cancellous bone remodelling allowing for efficient and effective numerical implementation. Therein, the macro-scale is governed by the well-established kinematics and kinetics of one-scale continuum bone remodelling. However, the constitutive behaviour is not postulated phenomenologically at the macro-scale, but rather follows from the meso-scale. There, for the sake of computational efficiency, the trabecular architecture is idealised as a truss network with the cross-sectional area of the trabeculae adapting to mechanical loading. Then, the meso- and the macro-scale are coupled through up- and down-scaling. Computational results on benchmark problems from bio-mechanics demonstrate that the proposed two-scale approach is effective from a modelling perspective and efficient from a computational perspective. In particular, it automatically captures anisotropy resulting from the irregular trabecular architecture at the meso-scale, and, most importantly, enables the direct investigation of different trabecular structures at the meso-scale, thereby serving as a virtual “magnifiying glass”. As an outlook, the proposed two-scale approach to cancellous bone remodelling provides an excellent launch pad for further extension, e.g., by considering more complex trabecular architectures and/or through inclusion of micro-scale bone cellular activities.

Exact Analytical Algorithm for the Solvent-Accessible Surface Area and Derivatives in Implicit Solvent Molecular Simulations on GPUs.

In this paper, we present differentiable solvent-accessible surface area (dSASA), an exact geometric method to calculate SASA analytically along with atomic derivatives on GPUs. The atoms in a molecule are first assigned to tetrahedra in groups of four atoms by Delaunay tetrahedralization adapted for efficient GPU implementation, and the SASA values for atoms and molecules are calculated based on the tetrahedralization information and inclusion-exclusion method. The SASA values from the numerical icosahedral-based method can be reproduced with >98% accuracy for both proteins and RNAs. Having been implemented on GPUs and incorporated into AMBER, we can apply dSASA to implicit solvent molecular dynamics simulations with the inclusion of this nonpolar term. The current GPU version of GB/SA simulations has been accelerated up to nearly 20-fold compared to the CPU version, outperforming LCPO, a commonly used, fast algorithm for calculating SASA, as the system size increases. While we focus on the accuracy of the SASA calculations for proteins and nucleic acids, we also demonstrate stable GB/SA MD mini-protein simulations.

Formulation and efficient implementation of coupled anisotropic damage-plasticity model for plain concrete

Formulation and efficient implementation of coupled anisotropic damage-plasticity model for plain concrete

Quasi-degenerate extension of local N-electron valence state perturbation theory with pair-natural orbital method based on localized virtual molecular orbitals.

Chemical phenomena involving near-degenerate electronic states, such as conical intersections or avoided crossing, can be properly described using quasi-degenerate perturbation theory. This study proposed a highly scalable quasi-degenerate second-order N-electron valence state perturbation theory (QD-NEVPT2) using the local pair-natural orbital (PNO) method. Our recent study showed an efficient implementation of the PNO-based state-specific NEVPT2 method using orthonormal localized virtual molecular orbitals (LVMOs) as an intermediate local basis. This study derived the state-coupling (or off-diagonal) terms to implement QD-NEVPT2 in an alternative manner to enhance efficiency based on the internally contracted basis and PNO overlap matrices between different references. To facilitate further acceleration, a local resolution-of-the-identity (RI) three-index integral generation algorithm was developed using LMOs and LVMOs. Although the NEVPT2 theory is considered to be less susceptible to the intruder-state problem (ISP), this study revealed that it can easily suffer from ISP when calculating high-lying excited states. We ameliorated this instability using the imaginary level shift technique. The PNO-QD-NEVPT2 calculations were performed on small organic molecules for the 30 lowest-lying states, as well as photoisomerization involving the conical intersection of 1,1-dimethyldibenzo[b,f] silepin with a cis-stilbene skeleton. These calculations revealed that the PNO-QD-NEVPT2 method yielded negligible errors compared to the canonical QD-NEVPT2 results. Furthermore, we tested its applicability to a large photoisomerization system using the green fluorescent protein model and the ten-state calculation of the large transition metal complex, showcasing that off-diagonal elements can be evaluated at a relatively low cost.

Neuromorphic place cells

A neuromorphic simultaneous localization and mapping (SLAM) system shows potential for more efficient implementation than its traditional counterpart. At the mean time a neuromorphic model of spatial encoding neurons in silicon could provide insights on the functionality and dynamic between each group of cells. Especially when realistic factors including variations and imperfections on the neural movement encoding are presented to challenge the existing hypothetical models for localization. We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, egocentric place cells, and the typical allocentric place cells. Together, they form a biologically plausible network that could reproduce the localization functionality of place cells observed in rodents. The system consists of a theta chip with 128 theta cell units and an FPGA implementing 4 networks for egocentric place cells formation that provides the capability for tracking on a 11 by 11 place cell grid. Experimental results validate the robustness of our model when suffering from as much as 18% deviation, induced by parameter variations in analog circuits, from the mathematical model of theta cells. We provide a model for implementing dynamic neuromorphic SLAM systems for dynamic-scale mapping of cluttered environments, even when subject to significant errors in sensory measurements and real-time analog computation. We also suggest a robust approach for the network topology of spatial cells that can mitigate neural non-uniformity and provides a hypothesis for the function of grid cells and the existence of egocentric place cells.

A CMOS-integrated terahertz near-field sensor based on an ultra-strongly coupled meta-atom

Recently, plasmonic-based sensors operating in the terahertz frequency range have emerged as perspective tools for rapid and efficient label-free biosensing applications. In this work, we present a fully electronic approach allowing us to achieve state-of-the-art sensitivity by utilizing a near-field-coupled electronic sensor. We demonstrate that the proposed concept enables the efficient implementation and probing of a so-called ultra-strongly coupled sub-wavelength meta-atom as well as a single resonant circuit, allowing to limit the volume of material under test down to a few picoliter range. The sensor has been monolithically integrated into a cost-efficient silicon-based CMOS technology. Our findings are supported by both numerical and analytical models and validated through experiments. They lay the groundwork for near-future developments, outlining the perspectives for a terahertz microfluidic lab-on-chip dielectric spectroscopy sensor.

Identification of congenital aortic valve malformations in juvenile natriuretic peptide receptor 2 deficient (Npr2+/-) mice using high frequency ultrasound.

Mouse models of congenital aortic valve malformations are useful for studying disease pathobiology, but most models have incomplete penetrance (e.g., ~2 to 77% prevalence of bicuspid aortic valves (BAVs) across multiple models). For longitudinal studies of pathologies associated with BAVs and other congenital valve malformations, which manifest over months in mice, it is operationally inefficient, economically burdensome, and ethically challenging to enroll large numbers of mice in studies without first identifying those with valvular abnormalities. To address this need, we established and validated a novel in vivo high frequency (30 MHz) ultrasound imaging protocol capable of detecting aortic valvular malformations in juvenile mice. Fifty natriuretic peptide receptor 2 heterozygous mice on a low density lipoprotein receptor deficient background (Npr2+/-;Ldlr-/-; 32 male, 18 female) were imaged at 4- and 8-weeks of age. Fourteen percent of the Npr2+/-;Ldlr-/- mice exhibited features associated with aortic valve malformations, including: i) abnormal trans-aortic flow patterns on color Doppler (recirculation and regurgitation); ii) peak systolic flow velocities distal to the aortic valves reaching or surpassing ~1250 mm/s by pulsed wave Doppler; and iii) putative fusion of cusps along commissures and abnormal movement elucidated by 2D imaging with ultra-high temporal resolution. Valves with these features were confirmed by ex vivo gross anatomy and histological visualization to have thickened cusps, partial fusions, or Sievers type 0 bicuspid valves. This ultrasound imaging protocol will enable efficient, cost-effective and humane implementation of studies of congenital aortic valvular abnormalities and associated pathologies in a wide range of mouse models.

Knowledge Assessment Patterns for Distant Education: The Perceived Impact on Grading, Motivation, and Satisfaction

With the increase in remote learning, the efficient implementation of distant knowledge assessments has become an essential topic. New challenges have arisen that have had to be adequately addressed and successfully solved. As a response, we introduced an assessment patterns catalogue for distant education, handling various challenges by proposing possible solutions. The catalogue presents a collection of proven practises targeting knowledge assessment in digital and distant environments. The paper presents the survey results from the final step of catalogue creation. The objective was to verify its suitability and expedite its use, focusing on several aspects of knowledge assessment. We focused on the perceived assessment patterns’ impact on grading objectivity and consistency, as well as students’ motivation and satisfaction with an implemented assessment, explored from the students’ and teachers’ perspectives. We gathered data using a uniform questionnaire distributed between students and teachers, both actively involved in distant knowledge assessment. Detailed data analysis highlighted the patterns with the highest perceived impact on the previously mentioned assessment aspects. We also analysed the top-rated patterns within the pattern categories. The results depict a high overlap between students’ and teachers’ perspectives, wherein patterns like Pentathlon, Statistical Validator, Game Rules, and Bonus Points were perceived as the patterns with the highest impact on grading objectivity and consistency, as well as the patterns with the most significant impact on students’ motivation and satisfaction.

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.

Polarization‐Enhanced Narrow‐Band GeS2 2‐D SWIR Spectral Phototransistor

AbstractIntegrated computational spectrometers with gate‐tunable nano heterostructures and reconstruction algorithms are attractive for on‐chip gas‐sensing spectrometers and have enabled versatile spectrum detectors. However, they require the selective and optical filtering capabilities of wavelengths, restricting their efficient implementation in narrow‐band photodetection. In this study, a printable spectral phototransistor is developed with high dynamic detectivity (1012 Jones and 105 Hz at −3 db bandwidth) modulated by a GeS2 nanosheet heterostructure at short‐wave infrared (SWIR) regime. Using the transport mode switching of carriers in a heterostructure and the polarization‐sensitivity of the GeS2 two‐dimension (2‐D) nanosheet, this SWIR spectral phototransistor demonstrates an accurate narrow‐band selective (96.7% accuracy) spectrum detector and performed a deep‐learning analysis of an artificial neural network (ANN). Furthermore, this GeS2 2‐D based spectral phototransistor, characterized by its high in‐plane anisotropy and electrically reconfigurable properties, extends the applicability of narrow‐band photodetection with 15 nm Full Width at Half Maximum (FWHM) to the recognition of trace‐gases at the parts per billion (ppb) level.

Kerr-effect-based quantum logical gates in decoherence-free subspace

The decoherence effect caused by the coupling between the system and the environment undoubtedly leads to the errors in efficient implementations of two (or three) qubit logical gates in quantum information processing. Fortunately, decoherence-free subspace (DFS) introduced can effectively decrease the influence of decoherence effect. In this paper, we propose some schemes for setting up a family of quantum control gates, including controlled-NOT (CNOT), Toffoli, and Fredkin gates for two or three logical qubits by means of cross-Kerr nonlinearities in DFS. These three logical gates require neither complicated quantum computational circuits nor auxiliary photons (or entangled states). The success probabilities of three logical gates are approximate 1 by performing the corresponding classical feed-forward operations based on the different measuring results of the X-homodyne detectors, and their fidelities are robust against the photon loss with the current technology. The proposed logical gates rely on only simple linear-optics elements, available single-qubit operations, and mature measurement methods, making our proposed gates be feasible and efficient in practical applications.

AN EFFICIENT VLSI IMPLEMENTATION OF EDGE DETECTION OF IMAGES

Edge detection is a fundamental process in image processing with significant implications across multiple domains. Within the realm of object detection, the accurate delineation of image perimeters forms the bedrock for subsequent analysis. In this study, we present an optimized Laplacian Edge Detection algorithm tailored for efficient implementation in hardware.Leveraging approximation methodologies and strategic pipelining, we streamline complex operations, thereby reducing latency and resource consumption. Our implementation, realized through the Verilog Hardware Description Language (VHDL), stands as a testament to the efficacy of our approach. Through rigorous optimization and meticulous comparison with conventional Sobel edge detection, we demonstrate superior performance metrics, including enhanced Area utilization, reduced Time Delay, and improved Power Efficiency. This work underscores the importance of methodological refinement and technological innovation in advancing edge detection techniques for practical applications. Keywords: Laplacian, Sobel, Kernel, Pixel range, Area, Power

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs

We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) hardware. Our approach leverages Hessian-aware quantization (HAWQ) of NNs, the Quantized Open Neural Network Exchange (QONNX) intermediate representation, and the hls4ml tool flow for transpiling NNs into FPGA firmware. This makes efficient NN implementations in hardware accessible to nonexperts, in a single open-sourced workflow that can be deployed for real-time machine-learning applications in a wide range of scientific and industrial settings. We demonstrate the workflow in a particle physics application involving trigger decisions that must operate at the 40 MHz collision rate of the CERN Large Hadron Collider (LHC). Given the high collision rate, all data processing must be implemented on FPGA hardware within the strict area and latency requirements. Based on these constraints, we implement an optimized mixed-precision NN classifier for high-momentum particle jets in simulated LHC proton-proton collisions.

Filtering After Shading With Stochastic Texture Filtering

2D texture maps and 3D voxel arrays are widely used to add rich detail to the surfaces and volumes of rendered scenes, and filtered texture lookups are integral to producing high-quality imagery. We show that applying the texture filter after evaluating shading generally gives more accurate imagery than filtering textures before BSDF evaluation, as is current practice. These benefits are not merely theoretical, but are apparent in common cases. We demonstrate that practical and efficient filtering after shading is possible through the use of stochastic sampling of texture filters. Stochastic texture filtering offers additional benefits, including efficient implementation of high-quality texture filters and efficient filtering of textures stored in compressed and sparse data structures, including neural representations. We demonstrate applications in both real-time and offline rendering and show that the additional error from stochastic filtering is minimal. We find that this error is handled well by either spatiotemporal denoising or moderate pixel sampling rates.