Efficient Computer Research Articles

PAN-SUNET: UTILITY CORRIDOR UNDERSTANDING USING SPATIAL LAYOUT CONSISTENCY

Abstract. The article addresses the need for a dependable and efficient computer vision system to examine utility networks with minimal human intervention, given the deteriorating state of these networks. To classify the dense and irregular point clouds obtained from the airborne laser terrain mapping (ALTM) system, which is used for data collection, we suggest a deep learning network named Panoptic-Semantic Utility Network (Pan-SUNet). The proposed network incorporates three networks to achieve voxel-based semantic segmentation and 3D object detection of the point clouds at various resolutions, including object categories in three dimensions, and predicts two-dimensional regional labels to differentiate utility and corridor regions from non-corridor regions. The network also ensures spatial layout consistency in the prediction of the voxel-based 3D network using regional segmentation. By testing the proposed approach on 67 km2 of utility corridor data with an average density of 5 pts/m2, the paper demonstrates the effectiveness of the technique. The proposed network outperforms the state-of-the-art baseline network, achieving an F1 score of 94% for the pylon class, 99% for the ground class, 99% for the vegetation class, and 99% for the powerline class. It also shows high performance for 3D object detection for pylon and span achieving average precision of 99% and 92% respectively.

Light-to-Heat Conversion of Optically Trapped Hot Brownian Particles.

Anisotropic hybrid nanostructures stand out as promising therapeutic agents in photothermal conversion-based treatments. Accordingly, understanding local heat generation mediated by light-to-heat conversion of absorbing multicomponent nanoparticles at the single-particle level has forthwith become a subject of broad and current interest. Nonetheless, evaluating reliable temperature profiles around a single trapped nanoparticle is challenging from all of the experimental, computational, and fundamental viewpoints. Committed to filling this gap, the heat generation of an anisotropic hybrid nanostructure is explored by means of two different experimental approaches from which the local temperature is measured in a direct or indirect way, all in the context of hot Brownian motion theory. The results were compared with analytical results supported by the numerical computation of the wavelength-dependent absorption efficiencies in the discrete dipole approximation for scattering calculations, which has been extended to inhomogeneous nanostructures. Overall, we provide a consistent and comprehensive view of the heat generation in optical traps of highly absorbing particles from the viewpoint of the hot Brownian motion theory.

Deep Learning Artificial Intelligence Tool for Automated Radiographic Determination of Posterior Tibial Slope in Patients With ACL Injury.

An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets. To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements. Cohort study (diagnosis); Level of evidence, 2. A deep learning U-Net model was developed on a cohort of 300 postoperative short-leg lateral radiographs from patients who underwent ACLR to segment the tibial shaft, tibial joint surface, and tibial tuberosity. The model was trained via a random split after an 80 to 20 train-validation scheme. Masks for training images were manually segmented, and the model was trained for 400 epochs. An image processing pipeline was then deployed to annotate and measure the PTS using the predicted segmentation masks. Finally, the performance of this combined pipeline was compared with human measurements performed by 2 study personnel using a previously validated manual technique for measuring the PTS on short-leg lateral radiographs on an independent test set consisting of both pre- and postoperative images. The U-Net semantic segmentation model achieved a mean Dice similarity coefficient of 0.885 on the validation cohort. The mean difference between the human-made and computer-vision measurements was 1.92° (σ = 2.81° [P = .24]). Extreme disagreements between the human and machine measurements, as defined by ≥5° differences, occurred <5% of the time. The model was incorporated into a web-based digital application front-end for demonstration purposes, which can measure a single uploaded image in Portable Network Graphics format in a mean time of 5 seconds. We developed an efficient and reliable deep learning computer vision algorithm to automate the PTS measurement on short-leg lateral knee radiographs. This tool, which demonstrated good agreement with human annotations, represents an effective clinical adjunct for measuring the PTS as part of the preoperative assessment of patients with ACL injuries.

Comprehensive holographic parallel beam modulation inside material based on automatic differentiation

Holographic beam modulation is widely applied in optical tweezers, hard-brittle material marking, high-density storage, etc. To generate high-fidelity arbitrary 3-dimensional (3D) parallel multifoci inside the material, the spherical aberration compensated automatic differentiation (SACAD) algorithm is presented. All polarization components are included and the spherical aberration compensation is embedded in the physical model. The technique of automatic differentiation is used in the error backpropagation procedure, ensuring efficient parallel computing of pixel-by-pixel gradients. In several simulation tests, the root mean square errors of the generated 3D multifoci distributions are all less than 0.01 and the diffraction efficiencies are all beyond 90%, outperforming the results of the established algorithms. In the experiments, we have verified the advantage of SACAD algorithm in complicated 3D internal marking with spherical aberration compensation. Since the SACAD algorithm can achieve high fidelity and efficiency phase retrieval with a straightforward procedure, it has the potential to become a well-received solution for internal parallel beam modulation.

SlimFL: Federated Learning With Superposition Coding Over Slimmable Neural Networks

Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices’ distributed computing capabilities. However, applying FL in practice is challenging due to the local devices’ heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions. To cope with these issues, this paper proposes a novel learning framework by integrating FL and width-adjustable slimmable neural networks (SNN). Integrating FL with SNNs is challenging due to time-varying channel conditions and data distributions. In addition, existing multi-width SNN training algorithms are sensitive to the data distributions across devices, which makes SNN ill-suited for FL. Motivated by this, we propose a communication and energy-efficient SNN-based FL (named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SlimFL</i> ) that jointly utilizes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">superposition coding (SC)</i> for global model aggregation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">superposition training (ST)</i> for updating local models. By applying SC, SlimFL exchanges the superposition of multiple-width configurations decoded as many times as possible for a given communication throughput. Leveraging ST, SlimFL aligns the forward propagation of different width configurations while avoiding inter-width interference during backpropagation. We formally prove the convergence of SlimFL. The result reveals that SlimFL is not only communication-efficient but also deals with non-IID data distributions and poor channel conditions, which is also corroborated by data-intensive simulations.

Energy-Efficient Computing and Green Computing Techniques

Energy-efficient computing and strategies for environmentally sustainable computing have become indispensable in the contemporary world, given the escalating demand for computing power and the growing emphasis on ecological conservation. This summary delves into diverse facets of energy efficient computing and green computing methodologies, elucidating their significance, hurdles, and potential remedies. The primary objective of energy-efficient computing is to curtail the energy consumption of computing systems while either maintaining or enhancing their performance. This proves pivotal in alleviating the ecological ramifications of computing, especially as data centers and similar facilities consume substantial amounts of energy. Green computing techniques encompass a broad spectrum of practices geared towards diminishing the environmental footprint of computing. These encompass the utilization of renewable energy sources, the crafting of energy-efficient hardware, the optimization of software algorithms, and the cultivation of energy consciousness among users. Despite the advantages associated with energy-efficient computing and green computing methodologies, various challenges necessitate attention. These encompass the delicate balance between energy efficiency and performance, the intricacies involved in designing energy-efficient systems, and the absence of standardized metrics for gauging and comparing energy efficiency. Exploration in this domain propels technological progress, resulting in the creation of more effective hardware, software, and systems. These advancements not only positively impact the environment but also propel the overall evolution of computing technology. The Information and Communication Technology (ICT) sector substantially adds to worldwide carbon emissions. Energy-efficient computing and environmentally conscious computing approaches can play a crucial role in mitigating this impact, decreasing energy usage, and advocating for the adoption of renewable energy sources. The aim of this research is to investigate the complexities associated with multiple attribute decision-making when confronted with intuitionist fuzzy information. In this context, the weights of attributes are not entirely known, and the attribute values are expressed using intuitionist fuzzy numbers. To ascertain the attribute weights, an optimization model is formulated based on the foundational principles of traditional grey relational analysis (GRA). The proposed approach entails computing the grey relation degree between each alternative and the positive-ideal solution as well as the negative-ideal solution. This degree is subsequently utilized to establish a relative relational degree, facilitating the simultaneous ranking of all alternatives concerning both the positive-ideal solution (PIS) and negative-ideal solution (NIS). From the result Efficient Algorithms is ranked at first position and Green Data Centers is ranked at fifth position.

Modeling of the solitary waves trajectories in “shallow water” environme

Solitary waves in the “shallow water” environment are considered and approximate approach to calculation and prediction of trajectories of these waves is proposed. We use an approach which was developed in recent years and was called the T-representations method. This method allows to move away from the exact solutions of the corresponding systems of differential equations and construct the trajectory approximately, in fact estimating only the point of maximum of the wave amplitude and its displacement. An appropriate mathematical simplification makes efficient computer simulations possible. In recent years, software packages have been actively used to monitor and predict a number of dangerous natural phenomena, such as Earth Alerts, Pacific Tsunami Warning System, Wave Monitoring Sites and others. But these systems do not take solitary waves into account. Using our approach, it is possible to refine the parameters of solitary waves in a specific environment, investigate whether such waves exist there at all and create functions taking into account solitary waves in monitoring systems.

Interface resistance-switching with reduced cyclic variations for reliable neuromorphic computing

As a synaptic device candidate for artificial neural networks (ANNs), memristors hold great promise for efficient neuromorphic computing. However, commonly used filamentary memristors normally exhibit large cyclic variations due to the stochastic nature of filament formation and ablation, which will inevitably degrade the computing accuracy. Here we demonstrate, in nanoscale Ag2S-based memristors that resistance-switching (RS) at the contact interface can be a promising solution to reduce cyclic variations. When the Ag2S memristor is operated with a filament-free interface RS via Schottky barrier height modification at the contact interface, it shows an ultra-small cycle-to-cycle variation of 1.4% during 104 switching cycles. This is in direct contrast to the variation of (28.9%) of the RS filament extracted from the same device. Interface RS can also emulate synaptic functions and psychological behavior. Its improved learning ability over a filament RS, with a higher saturated accuracy approaching 99.6%, is finally demonstrated in a simplified ANN.

Efficient Maximum k-Defective Clique Computation with Improved Time Complexity

k-defective cliques relax cliques by allowing up-to k missing edges from being a complete graph. This relaxation enables us to find larger near-cliques and has applications in link prediction, cluster detection, social network analysis and transportation science. The problem of finding the largest k-defective clique has been recently studied with several algorithms being proposed in the literature. However, the currently fastest algorithm KDBB does not improve its time complexity from being the trivial O(2n), and also, KDBB's practical performance is still not satisfactory. In this paper, we advance the state of the art for exact maximum k-defective clique computation, in terms of both time complexity and practical performance. Moreover, we separate the techniques required for achieving the time complexity from others purely used for practical performance consideration; this design choice may facilitate the research community to further improve the practical efficiency while not sacrificing the worst case time complexity. In specific, we first develop a general framework kDC that beats the trivial time complexity of O(2n) and achieves a better time complexity than all existing algorithms. The time complexity of kDC is solely achieved by our newly designed non-fully-adjacent-first branching rule, excess-removal reduction rule and high-degree reduction rule. Then, to make kDC practically efficient, we further propose a new upper bound, two new reduction rules, and an algorithm for efficiently computing a large initial solution. Extensive empirical studies on three benchmark graph collections with 290 graphs in total demonstrate that kDC outperforms the currently fastest algorithm KDBB by several orders of magnitude.

Computation via Neuron-like Spiking in Percolating Networks of Nanoparticles.

The biological brain is a highly efficient computational system in which information processing is performed via electrical spikes. Neuromorphic computing systems that work on similar principles could support the development of the next generation of artificial intelligence and, in particular, enable low-power edge computing. Percolating networks of nanoparticles (PNNs) have previously been shown to exhibit critical spiking behavior, with promise for highly efficient natural computation. Here we employ a rate coding scheme to show that PNNs can perform Boolean operations and image classification. Near perfect accuracy is achieved in both tasks by manipulating the spiking activity using certain control voltages. We demonstrate that the key to successful computation is that nanoscale tunnel gaps within the percolating networks transform input data through a powerful modulus-like nonlinearity. These results provide a basis for implementation of further computational schemes that exploit the brain-like criticality of these networks.

Heterogeneous Integration with 3D Packaging

The next generation of competitive integrated devices demand increased device density, higher memory bandwidth, reduced global interconnects, increased energy efficiency (with higher performance), and a smaller footprint. With the slowdown in Moore’s law and the advancements in chiplet architectures, which are now recognized as fundamental to enabling the continued economically viable growth of power efficient computing, advanced packaging technologies and architectures are becoming more critical to enabling Moore’s Law’s next frontier through heterogeneous integration. In this presentation, we will cover the advanced package architectures being enabled by AMD to enable power, performance, area, and cost (PPAC) improvements as well as enable heterogeneous architectures. The direct Cu-Cu bonding technology used in AMD 3D V-Cache architecture will be detailed and compared to industry standard 3D architectures.

Emerging Robust Polymer Materials for High-Performance Two-Terminal Resistive Switching Memory.

Facing the era of information explosion and the advent of artificial intelligence, there is a growing demand for information technologies with huge storage capacity and efficient computer processing. However, traditional silicon-based storage and computing technology will reach their limits and cannot meet the post-Moore information storage requirements of ultrasmall size, ultrahigh density, flexibility, biocompatibility, and recyclability. As a response to these concerns, polymer-based resistive memory materials have emerged as promising candidates for next-generation information storage and neuromorphic computing applications, with the advantages of easy molecular design, volatile and non-volatile storage, flexibility, and facile fabrication. Herein, we first summarize the memory device structures, memory effects, and memory mechanisms of polymers. Then, the recent advances in polymer resistive switching materials, including single-component polymers, polymer mixtures, 2D covalent polymers, and biomacromolecules for resistive memory devices, are highlighted. Finally, the challenges and future prospects of polymer memory materials and devices are discussed. Advances in polymer-based memristors will open new avenues in the design and integration of high-performance switching devices and facilitate their application in future information technology.

Advanced Packaging: Enabling Moore’s Law’s Next Frontier Through Heterogeneous Integration

With chiplet architectures becoming mainstream, and recognized as fundamental to enabling the continued economically viable growth of power efficient computing, advanced packaging technologies and architectures are becoming more critical to enabling Moore’s Law’s next frontier through heterogeneous integration. In this tutorial, we will cover the advanced package architectures being enabled by AMD to enable PPAC (power, performance, area and cost) improvements as well as enable heterogeneous architectures. The direct Cu- Cu bonding technology used in AMD’s 3D VCache architecture will be detailed and compared to industry standard 3D architectures for PPAC benefits. Other technologies that are being enabled to advance high performance computing architectures will also be previewed.

Power efficient MoS2 synaptic devices based on Maxwell–Wagner interfacial charging in binary oxides

Synaptic devices with tunable weight hold great promise in enabling non-von Neumann architecture for energy efficient computing. However, conventional metal-insulator-metal based two-terminal memristors share the same physical channel for both programming and reading, therefore the programming power consumption is dependent on the synaptic resistance states and can be particularly high when the memristor is in the low resistance states. Three terminal synaptic transistors, on the other hand, allow synchronous programming and reading and have been shown to possess excellent reliability. Here we present a binary oxide based three-terminal MoS2 synaptic device, in which the channel conductance can be modulated by interfacial charges generated at the oxide interface driven by Maxwell-Wagner instability. The binary oxide stack serves both as an interfacial charge host and gate dielectrics. Both excitatory and inhibitory behaviors are experimentally realized, and the presynaptic potential polarity can be effectively controlled by engineering the oxide stacking sequence, which is a unique feature compared with existing charge-trap based synaptic devices and provides a new tuning knob for controlling synaptic device characteristics. By adopting a three-terminal transistor structure, the programming channel and reading channel are physically separated and the programming power consumption can be kept constantly low (∼50 pW) across a wide dynamic range of 105. This work demonstrates a complementary metal oxide semiconductor compatible approach to build power efficient synaptic devices for artificial intelligence applications.

Nanowire integration in silica based integrated optical circuits: Limitations and challenges towards quantum computing

Optical integrated circuits suggest a very promising platform for the development of robust and efficient quantum computers. A critical issue to their development and scalability is the integration of multiple single photon sources in the circuits. One major category of single photon sources is based on quantum dots that are embedded in semiconductor optical nanowires (NWQD) that allow their accurate handing and deterministic integration. Successful integrations of such nanowires have been reported in high index platforms like silicon or silicon nitride, with adequate coupling efficiency due the modal characteristics compatibility. On the other hand, Silica-on-Silicon is a major integration platform, which combined with new fabrication approaches like direct laser writing, for the definition of optical structures with refractive index modification, can provide the fabrication of highly optimized and tailor-made circuits by rapid prototyping. Considering for the first time the integration scenario of NWQD in laser written silica-based waveguides it is shown that the low (∼10-3) achievable refractive index contrast imposes strict limitations on the compatibility of such waveguides with NWQD resulting in general in low coupling efficiency. By considering several design and fabrication issues, suitable integration approaches with adequate efficiency are demonstrated, while also the limitations and challenges are revealed thus triggering new research directions.

Machine Learning-Based Hand Sign Recognition

Machine Learning-Based Hand Sign Recognition is a project aimed at developing a robust and efficient system for recognizing hand signs and gestures using state-of-the-art machine learning techniques. Hand sign recognition holds significant potential in a wide range of applications, including communication assistance for individuals with hearing impairments, human-computer interaction, and automation in various industries. This project leverages deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze and classify hand signs and gestures. The system is trained on a diverse dataset of hand signs, encompassing different gestures, poses, and lighting conditions, to ensure its adaptability and reliability in real-world scenarios. The project's success is contingent on the fine-tuning of machine learning models, thorough testing, and the incorporation of efficient computer vision techniques. Through rigorous evaluation and validation, we aim to achieve high accuracy and real-time performance. As a result, Machine Learning-Based Hand Sign Recognition has the potential to improve accessibility and convenience for those with hearing impairments, enhance human-computer interaction, and contribute to advancements in automation and robotics across multiple domains

A comparison of feature extraction methods in image stitching

The usage of feature detectors for image stitching has become a popular research area in computer vision. Various feature extraction algorithms can be used in the process of image stitching process, but they perform varyingly when handling different images and no single algorithm could outperform all others. This paper focuses on the comparison of feature extraction algorithms used for panoramic image stitching. The research utilizes the SIFT, ORB, AKAZE, and BRISK to conduct feature points and match feature points on a group of image sets. The RANSAC algorithm is then used to filter out the outliers and calculate the homograph matrix. Completes the panoramic with image splicing and smoothing through the matrix transformation. Derived from the comparison of the matching and stitching results, the AKAZE detector is found to be the fastest feature point detection and extraction algorithm, while the SIFT detector will provide more feature points to make more accurate matches possible. These findings have implications for the development of efficient and effective computer vision technologies for various applications.

High-order moment closure models with random batch method for efficient computation of multiscale turbulent systems.

We propose a high-order stochastic-statistical moment closure model for efficient ensemble prediction of leading-order statistical moments and probability density functions in multiscale complex turbulent systems. The statistical moment equations are closed by a precise calibration of the high-order feedbacks using ensemble solutions of the consistent stochastic equations, suitable for modeling complex phenomena including non-Gaussian statistics and extreme events. To address challenges associated with closely coupled spatiotemporal scales in turbulent states and expensive large ensemble simulation for high-dimensional systems, we introduce efficient computational strategies using the random batch method (RBM). This approach significantly reduces the required ensemble size while accurately capturing essential high-order structures. Only a small batch of small-scale fluctuation modes is used for each time update of the samples, and exact convergence to the full model statistics is ensured through frequent resampling of the batches during time evolution. Furthermore, we develop a reduced-order model to handle systems with really high dimensions by linking the large number of small-scale fluctuation modes to ensemble samples of dominant leading modes. The effectiveness of the proposed models is validated by numerical experiments on the one-layer and two-layer Lorenz '96 systems, which exhibit representative chaotic features and various statistical regimes. The full and reduced-order RBM models demonstrate uniformly high skill in capturing the time evolution of crucial leading-order statistics, non-Gaussian probability distributions, while achieving significantly lower computational cost compared to direct Monte-Carlo approaches. The models provide effective tools for a wide range of real-world applications in prediction, uncertainty quantification, and data assimilation.

A Unified Parallel CORDIC-Based Hardware Architecture for LSTM Network Acceleration

Deep Neural Networks (DNNs) have recently become the standard tool for solving various practical problems in a wide range of applications with state-of-the-art performance. Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) are a subset of DNNs with fully connected single or multi-layer networks. The complex neurons and internal states of LSTM networks enable them to build a memory of events, making them ideal for time series applications. Despite the great potential of LSTM networks, their heterogeneous operations and computational resource requirements create a vast gap when it comes to the fast processing time required in real-time applications using low-power, low-cost edge devices. This work proposes a novel hardware architecture that combines serial-parallel computation with matrix algebra concepts and efficient low-power computer arithmetics for LSTM network acceleration. The hardware is based on a systolic ring of outer-product-based processing elements (PEs) and a reusable single activation function block (AFB). PEs and AFB are implemented using the coordinate rotation digital computer algorithm (CORDIC) in the linear and hyperbolic modes. Unlike most approaches, the proposed hardware can be configured to perform recurrent and non-recurrent fully connected layers (FC) computations, making it suitable for various low-power edge applications. The architecture is validated on the Xilinx PYNQ-Z1 development board using an open-source time series dataset. The implemented design achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$114 \mu s$</tex-math></inline-formula> average latency and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.8GOPS$</tex-math></inline-formula> throughput. The proposed design's low latency and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.438W$</tex-math></inline-formula> power consumption makes it suitable for resource-constrained edge platforms.

Energy efficient computing by using of software optimization aimed on execution time

Despite of all hardware capabilities, optimization of software always was one of actual tasks. Usually optimization of software was aimed to reduce an execution time. But recent decades it’s possible sometimes also to reduce energy consumption and/or energy efficiency of computing because of enhanced energy saving capabilities. Indeed, if computations are made faster, CPU can suspend unusual computational and control blocks. In case of parallelization which is one of optimization techniques, power consumption can raise in short periods of computations because of using multiple CPUs simultaneously. At once execution time can be decreased dramatically so energy efficiency of computing still increasing. In the paper the authors provide the experiments on small size mini PC Raspberry Pi 3B which show that using of optimization tiling method not only speed up a processing but also increase energy efficiency of computing. For such low power systems this can be useful to increase power on time with a battery power source.