Computational Power Research Articles

A Novel Method for Optimising Energy Efficiency in High-Performance Computing Systems

This paper introduces a new method to promote energy efficiency in computing systems. The rapidly growing demand for computational power in high-performance computing (HPC) systems is accompanied by a significant increase in energy consumption. This research investigates the many ways to minimise energy consumption in HPC systems without sacrificing computational performance. Building upon previous research we combined and refined ideas to develop an optimised approach. Our method utilises a tri-modular framework incorporating an Energy-Efficient Hardware Design, Resource Management, and Optimisation, and Server Virtualisation. The first module employs a method of designing smart energy-efficient hardware. This method incorporates leakage reduction techniques such as clock gating and sleep states. The second module uses a technique focused on optimising server software. This method is based on Dynamic Voltage and frequency scaling (DVFS) for power management in data centres. The third module is based on server virtualisation and employs software to create multiple virtual machines on a single physical server allowing for a significant reduction in energy and hardware costs. The methods used in these three modules are systematically integrated to produce a more efficient consumption of electricity. This will allow for the computing system to minimise energy consumption without compromising computational power.

Acceleration of Tensor-Product Operations with Tensor Cores

In this paper, we explore the acceleration of tensor product operations in finite element methods, leveraging the computational power of the NVIDIA A100 GPU Tensor Cores. We provide an accessible overview of the necessary mathematical background and discuss our implementation strategies. Our study focuses on two common programming approaches for NVIDIA Tensor Cores: the C++ Warp Matrix Functions in nvcuda::wmma and the inline Parallel Thread Execution (PTX) instructions mma.sync.aligned . A significant focus is placed on the adoption of the versatile inline PTX instructions combined with a conflict-free shared memory access pattern, a key to unlocking superior performance. When benchmarked against traditional CUDA Cores, our approach yields a remarkable 2.3-fold increase in double precision performance, achieving 8 TFLOPS/s—45% of the theoretical maximum. Furthermore, in half-precision computations, numerical experiments demonstrate a fourfold enhancement in solving the Poisson equation using the flexible GMRES (FGMRES) method, preconditioned by a multigrid method in 3D. This is achieved while maintaining the same discretization error as observed in double precision computations. These results highlight the considerable benefits of using Tensor Cores for finite element operators with tensor products, achieving an optimal balance between computational speed and precision.

Advancements in TinyML: Applications, Limitations, and Impact on IoT Devices

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricting their use to high-capability devices. With advancements in embedded system technology and the substantial development in the Internet of Things (IoT) industry, there is a growing desire to integrate ML techniques into resource-constrained embedded systems for ubiquitous intelligence. This aspiration has led to the emergence of TinyML, a specialized approach that enables the deployment of ML models on resource-constrained, power-efficient, and low-cost devices. Despite its potential, the implementation of ML on such devices presents challenges, including optimization, processing capacity, reliability, and maintenance. This article delves into the TinyML model, exploring its background, the tools that support it, and its applications in advanced technologies. By understanding these aspects, we can better appreciate how TinyML is transforming the landscape of AI and ML in embedded and IoT systems.

Performance Evaluation of Deep Learning Models for Dental Caries Classification via Panoramic Radiograph Images

Objective: The purpose of this study is to evaluate the ability of deep learning models to classify mandibular molar teeth according to the presence and proximity of caries to the dental pulp. This research summarizes the progress of artificial intelligence and potential dental problems in diagnosis, treatment, and disease prediction in medicine. It discusses data limitations, computational power, ethical considerations, and their implications for dentists. This can lay the groundwork for future research in this rapidly expanding field. Methods: The dataset used in this study consists of 1200 panoramic radiographs, which have been evaluated and classified into three categories: free of dental caries, coded as (H); enamel-dentin caries lesions treated with restorative filling, coded as (R); and deep dental caries that underwent root canal treatment, coded as (E). The images are prepared for the training-testing process using the k-fold crossevaluation technique and then fed into the pre-trained deep learning models for classification. Results: The VGG-19 model achieved superior results compared to the other models, with macro-average scores of 0.9111 for precision, 0.9127 for recall, and 0.9115 for f1-score, respectively. Conclusion: The promising results obtained in this study give confidence in endorsing the use of deep learning models in the dental treatments sector.

Analytical approach to piezoelectric model synthesis with the use of Cauer’s method for system design

Background Piezoceramic materials have unique property which enables direct and bilateral conversion between mechanical and electrical energy. This ability facilitates significant miniaturisation of technology and opens many opportunities in design of new actuators and energy harvesters. Mathematical modelling of piezoelectric modules is notoriously hard due to complex constitutive equations defining mechanical and electrical energy conversion. Methods The article presents research on a new synthesis method based on the Cauer’s method. Mechanical damping is introduced with the use of Rayleigh’s approximation. A discrete electromechanical model is formed based on the Mason’s piezoelectric model. The proposed approach allows modelling of piezoelectric systems based on a set of characteristic frequencies. The method allows a more general approach to the problem of modelling new systems, as opposed to application-oriented methods seen in literature. A non-standard model analysis method using edge graphs and structural numbers is also verified as a potential alternative for matrix-based method. The authors compare their precision and computation requirements. Results The structural method of mechanical model analysis gave identical results as the reference matrix method. However, the non-classical algorithm took much longer to calculate and was using more memory. The electromechanical model analysis has shown an error of 5% in comparison to resonance frequencies taken from a reference plate specification. The calculated magnitude of displacement was well above the capability of a 3.5mm thick piezoelectric plate. Conclusions The synthesis method presented in this paper allows synthesizing piezoelectric cascade models based on limited information in form of characteristic frequencies. Currently this method allows a coarse approximation of the real piezoelectric parameters with limited number of inputs. The additional method of analysis based on structural numbers offers a promising alternative to matrix calculations but requires a more thorough investigation of the computational power required to determine whether it can compete with existing algorithms.

Advanced petrographic thin section segmentation through deep learning-integrated adaptive GLFIF

In geological research, precise segmentation of sandstone thin sections is crucial for detailed subsurface material analysis. Traditional methods often fall short in accurately capturing the complexities of these samples. This study presents an innovative segmentation approach that integrates an adaptive Global and Local Fuzzy Image Fitting (GLFIF) algorithm with Otsu's thresholding, significantly enhancing segmentation accuracy and efficiency. Our method combines deep learning and traditional image processing techniques. The adaptive GLFIF algorithm, powered by deep learning, automates parameter tuning, thereby reducing manual intervention and improving precision. Unlike conventional methods that learn fixed parameters, our model dynamically adjusts the segmentation process to achieve accurate results. The dual-phase segmentation strategy effectively isolates small features and handles intricate boundaries, ensuring high-quality outcomes. Experimental results demonstrate that our approach improves segmentation accuracy by 11.2% (from 82.6% to 93.8%), the Jaccard index by 15.4% (from 76.8% to 92.2%), and the Dice coefficient by 9% (from 86.9% to 95.9%) compared to traditional methods. This technique bridges the gap between conventional image analysis and deep learning, combining precise segmentation with the automation and computational power of advanced algorithms. Our segmentation algorithm represents a significant advancement in automated petrographic thin section analysis. Traditional image processing methods, such as thresholding and level sets, excel in handling small objects and complex boundaries but require significant manual intervention and cannot achieve full automation. Recent deep learning methods, particularly semantic segmentation, offer end-to-end automation but struggle with small targets and intricate boundaries. Our approach effectively combines the strengths of both methodologies, providing a comprehensive and efficient solution for geological image analysis that ensures both high accuracy and full automation.

Multiple-base Logarithmic Quantization and Application in Reduced Precision AI Computations

The power of logarithmic quantizations and computations has been recognized as a useful tool in optimizing the performance of large ML models. There are plenty of applications of ML techniques in digital preservation. The accuracy of computations may play a crucial role in the corresponding algorithms. In this article, we provide results that demonstrate significantly better quantization signal-to-noise ratio performance thanks to multiple-base logarithmic number systems (MDLNS) in comparison with the floating point quantizations that use the same number of bits. On a hardware level, we present details about our Xilinx VCU-128 FPGA design for dot product and matrix vector computations. The MDLNS matrix-vector design significantly outperforms equivalent fixed-point binary designs in terms of area (A) and time (T) complexity and power consumption as evidenced by a 4 × scaling of AT 2 metric for VLSI performance, and 57% increase in computational throughput per watt compared to fixed-point arithmetic.

Detection of Alzheimer’s disease using pre-trained deep learning models through transfer learning: a review

Due to the progress in image processing and Artificial Intelligence (AI), it is now possible to develop automated tool for the early detection and diagnosis of Alzheimer’s Disease (AD). Handcrafted techniques developed so far, lack generality, leading to the development of deep learning (DL) techniques, which can extract more relevant features. To cater for the limited labelled datasets and requirement in terms of high computational power, transfer learning models can be adopted as a baseline. In recent years, considerable research efforts have been devoted to developing machine learning-based techniques for AD detection and classification using medical imaging data. This survey paper comprehensively reviews the existing literature on various methodologies and approaches employed for AD detection and classification, with a focus on neuroimaging techniques such as structural MRI, PET, and fMRI. The main objective of this survey is to analyse the different transfer learning models that can be used for the deployment of deep convolution neural network for AD detection and classification. The phases involved in the development namely image capture, pre-processing, feature extraction and selection are also discussed in the view of shedding light on the different phases and challenges that need to be addressed. The research perspectives may provide research directions on the development of automated applications for AD detection and classification.

AL-MobileNet: a novel model for 2D gesture recognition in intelligent cockpit based on multi-modal data

As the degree of automotive intelligence increases, gesture recognition is gaining more attention in human-vehicle interaction. However, existing gesture recognition methods are computationally intensive and perform poorly in multi-modal sensor scenarios. This paper proposes a novel network structure, AL-MobileNet (MobileNet with Attention and Lightweight Modules), which can quickly and accurately estimate 2D gestures in RGB and infrared (IR) images. The innovations of this paper are as follows: Firstly, to enhance multi-modal data, we created a synthetic IR dataset based on real 2D gestures and employed a coarse-to-fine training approach. Secondly, to speed up the model's computation on edge devices, we introduced a new lightweight computational module called the Split Channel Attention Block (SCAB). Thirdly, to ensure the model maintains accuracy in large datasets, we incorporated auxiliary networks and Angle-Weighted Loss (AWL) into the backbone network. Experiments show that AL-MobileNet requires only 0.4 GFLOPs of computational power and 1.2 million parameters. This makes it 1.5 times faster than MobileNet and allows for quick execution on edge devices. AL-MobileNet achieved a running speed of up to 28 FPS on the Ambarella CV28. On both general datasets and our dataset, our algorithm achieved an average PCK0.2 score of 0.95. This indicates that the algorithm can quickly generate accurate 2D gestures. The demonstration of the algorithm can be reviewed in gesturebaolong.

LB-CLAS: Lattice-based conditional privacy-preserving certificateless aggregate signature scheme for VANET

The rapid development of vehicular ad-hoc networks (VANETs) has brought great convenience to intelligent transportation, and the secure transmission of information in VANETs has become a serious problem. In addition, the protection of private information of vehicles is also a key issue. Aiming at the problem of how to guarantee the secure transmission of information in VANETs under the condition of satisfying security and privacy, we propose a lattice-based conditional privacy-preserving certificateless aggregate signature scheme (LB-CLAS) for VANETs. Instead of using Number Theory Research Unit (NTRU) lattices and discrete Gaussian sampling, the proposed LB-CLAS scheme is based on algebraic lattice. In addition, based on the module version of Small Integer Solution (MSIS) and module version of Learning With Error (MLWE) hard problems, we prove that the LB-CLAS scheme is existential unforgeability under adaptively chosen message attacks (EUF-CMA). Our LB-CLAS scheme employs individual signature verification in vehicle-to-vehicle (V2V) mode, while utilizing aggregate signatures and batch verification in vehicle-to-infrastructure (V2I) mode, with slightly differing transmission parameters between the two modes. Based on Dilithium, our LB-CLAS scheme solves the problem of high storage overhead and computational cost of existing schemes. The performance analysis shows that our LB-CLAS scheme is more efficient in terms of computation cost, storage overhead, and power consumption compared to existing schemes. Compared with existing schemes, our LB-CLAS scheme reduces the signature and verification overheads by more than 17.6% and 43.4%, respectively. Our LB-CLAS program also has significant advantages in batch verification. As the number of vehicles increases, our batch certification time cost is reduced by more than 90%. In addition, our LB-CLAS scheme has the smallest signature length, with a signature size that is 1X smaller than the most efficient existing scheme for the same level of security.

NinjaNIRS: an open hardware solution for wearable whole-head high-density functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) technology has been steadily advancing since the first measurements of human brain activity over 30 years ago. Initially, efforts were focused on increasing the channel count of fNIRS systems and then to moving from sparse to high density arrays of sources and detectors, enhancing spatial resolution through overlapping measurements. Over the last ten years, there have been rapid developments in wearable fNIRS systems that place the light sources and detectors on the head as opposed to the original approach of using fiber optics to deliver the light between the hardware and the head. The miniaturization of the electronics and increased computational power continues to permit impressive advances in wearable fNIRS systems. Here we detail our design for a wearable fNIRS system that covers the whole head of an adult human with a high-density array of 56 sources and up to 192 detectors. We provide characterization of the system showing that its performance is among the best in published systems. Additionally, we provide demonstrative images of brain activation during a ball squeezing task. We have released the hardware design to the public, with the hope that the community will build upon our foundational work and drive further advancements.

Accelerating Relativistic Exact-Two-Component Density Functional Theory Calculations with Graphical Processing Units.

Numerical integration of the exchange-correlation potential is an inherently parallel problem that can be significantly accelerated by graphical processing units (GPUs). In this Letter, we present the first implementation of GPU-accelerated exchange-correlation potential in the GauXC library for relativistic, 2-component density functional theory. By benchmarking against copper, silver, and gold coinage metal clusters, we demonstrate the speed and efficiency of our implementation, achieving significant speedup compared to CPU-based calculations. One GPU card provides computational power equivalent to roughly 400 CPU cores in the context of this work. The speedup further increases for larger systems, highlighting the potential of our approach for future, more computationally demanding simulations. Our implementation supports arbitrary angular momentum basis functions, enabling the simulation of systems with heavy elements and providing substantial speedup to relativistic electronic structure calculations. This advancement paves the way for more efficient and extensive computational studies in the field of density functional theory.

Seven quick tips for gene-focused computational pangenomic analysis

Pangenomics is a relatively new scientific field which investigates the union of all the genomes of a clade. The word pan means everything in ancient Greek; the term pangenomics originally regarded genomes of bacteria and was later intended to refer to human genomes as well. Modern bioinformatics offers several tools to analyze pangenomics data, paving the way to an emerging field that we can call computational pangenomics. Current computational power available for the bioinformatics community has made computational pangenomic analyses easy to perform, but this higher accessibility to pangenomics analysis also increases the chances to make mistakes and to produce misleading or inflated results, especially by beginners. To handle this problem, we present here a few quick tips for efficient and correct computational pangenomic analyses with a focus on bacterial pangenomics, by describing common mistakes to avoid and experienced best practices to follow in this field. We believe our recommendations can help the readers perform more robust and sound pangenomic analyses and to generate more reliable results.

Uncertainty quantification and confidence intervals for naive rare-event estimators

Abstract We consider the estimation of rare-event probabilities using sample proportions output by naive Monte Carlo or collected data. Unlike using variance reduction techniques, this naive estimator does not have an a priori relative efficiency guarantee. On the other hand, due to the recent surge of sophisticated rare-event problems arising in safety evaluations of intelligent systems, efficiency-guaranteed variance reduction may face implementation challenges which, coupled with the availability of computation or data collection power, motivate the use of such a naive estimator. In this paper we study the uncertainty quantification, namely the construction, coverage validity, and tightness of confidence intervals, for rare-event probabilities using only sample proportions. In addition to the known normality, Wilson, and exact intervals, we investigate and compare them with two new intervals derived from Chernoff’s inequality and the Berry–Esseen theorem. Moreover, we generalize our results to the natural situation where sampling stops by reaching a target number of rare-event hits. Our findings show that the normality and Wilson intervals are not always valid, but they are close to the newly developed valid intervals in terms of half-width. In contrast, the exact interval is conservative, but safely guarantees the attainment of the nominal confidence level. Our new intervals, while being more conservative than the exact interval, provide useful insights into understanding the tightness of the considered intervals.

Advancing the Quantum Internet: Integration of Quantum Machine Learning for Enhanced Efficiency and Reliability

Advancing the quantum internet through integrating Quantum Machine Learning (QML) presents a novel approach to addressing the significant challenges in quantum communication networks. Quantum internet, grounded in the principles of quantum mechanics, offers unparalleled security and computational power but faces hurdles such as error correction, efficient routing, and infrastructure maintenance. This paper first explores the foundational concepts that underpin the quantum internet, highlighting how quantum entanglement, superposition, and teleportation contribute to its potential. In the second half, this paper introduces QML techniques to enhance these systems, proposing adaptive error correction, optimized routing, and predictive maintenance as key innovations. These insights aim to improve quantum networks' efficiency, reliability, and scalability, positioning QML as a crucial element in the future development of global quantum communication systems.

Komodo Dragon Mlipir Algorithm-based CNN Model for Detection of Illegal Tree Cutting in Smart IoT Forest Area

Introduction: Trees and woods are vital to preventing climate change and protecting our planet. Sadly, they are constantly being destroyed due to human activities like deforestation, fires, etc. Method: This research presents and examines an outline for using audio event categorisation to automatically detect unlawful tree-cutting activity in forests. To monitor large swaths of forest, the research team proposes using ultra-low-power, minor devices incorporating edgecomputing microcontrollers and long-range wireless communication. An efficient and accurate audio classification solution based on multi-layer perceptron (MLP) and modified convolutional neural networks (M-CNN) is projected and tailored for cutting. The Komodo Dragon Mlipir Algorithm (KDMA) is used to pick the best weight for the CNN. Result: Compared to earlier efforts, the suggested system uses a computing technique to recognise deforestation-related hazards. Various preprocessing methods have been evaluated, with special attention paid to the trade-off between classification precision and computer resources, memory, and power use. Conclusion: Additionally, there have been long-range communication trials performed in natural settings. The experimental consequences demonstrate that the suggested method can notice and apprise tree-cutting occurrences through smart IoT for efficient and lucrative forest nursing.

Globalization of distributed parameter self‐optimizing control

AbstractNumerous nonlinear distributed parameter systems (DPSs) operate within an extensive range due to process uncertainties. Their spatial distribution characteristic, combined with nonlinearity and uncertainty, poses challenges in optimal operation under two‐step real‐time optimization (RTO) and economic model predictive control (EMPC). Both methods necessitate substantial computational power for prompt online reoptimization. Recent local distributed parameter self‐optimizing control (DPSOC) achieves optimality without repetitive optimization. However, its effectiveness is confined to a narrow range around a nominal operation. Here, globalized DPSOC is introduced to surmount the limitation of the local DPSOC. A global loss functional concerning controlled variables (CVs) is formulated using linear operators and Fubini's theorem. Minimizing the loss with a numerical optimization procedure yields CVs exhibiting global optimality. Maintaining these CVs at constants ensures such a process has a minimal average loss in a large operating space. The effectiveness of the proposed method is substantiated through a transport reaction simulation.

Quantum state synthesis: relation with decision complexity classes and impossibility of error reduction

This work investigates the relationships between quantum state synthesis complexity classes (a recent concept in computational complexity that focuses on the complexity of preparing quantum states) and traditional decision complexity classes. We especially investigate the role of the \emph{synthesis error parameter}, which characterizes the quality of the synthesis in quantum state synthesis complexity classes.We first show that in the high synthesis error regime, collapse of synthesis classes implies collapse of the equivalent decision classes. For more reasonable synthesis error, we then show a similar relationships for $\BQP$ and $\QCMA$. Finally, we show that for quantum state synthesis classes it is in general impossible to improve the quality of the synthesis: unlike the completeness and soundness parameters (which can be improved via repetition), the synthesis error cannot be reduced, even with arbitrary computational power.

Enhanced you only look once approach for automatic phytoplankton identification

<p>Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of ACVIT-YOLO compared to the YOLOv8m model, further demonstrated by the decreased giga floating-point operations (GFLOP), decreased parameter count, and compact dimensions, significantly improves the automation of phytoplankton species identification. This suggests that the ACVIT-YOLO model could produce a better prediction system for identifying phytoplankton with similar accuracy to the original YOLOv8m model but with lower computational power and resource usage.</p>

Case studies of the winds in the urban area of Hong Kong – Microclimate station observations and high resolution numerical simulations

Hong Kong, renowned for its densely packed urban areas, poses unique challenges for understanding the effects of buildings on local meteorological conditions. To address this, the Hong Kong Observatory has started building a network of urban meteorological monitoring stations since 2017 for monitoring, analysing and studying urban microclimate. This paper presents an observational and numerical study focusing on wind measurements obtained from wind sensors installed on two smart lampposts in Tsim Sha Tsui, a major urban area in Hong Kong. Two representative high wind conditions in Hong Kong, Super Typhoon Saola in 2023 and a strong monsoon case characterized by prevailing easterly winds, are considered. With the use of high resolution computational fluid dynamic simulations, major features of actual observations can be reproduced. This suggests that district scale or even street scale weather services could be possible in the future with sufficient computational power.