Computational Demands Research Articles

Simplified Knowledge Distillation for Deep Neural Networks Bridging the Performance Gap with a Novel Teacher–Student Architecture

The rapid evolution of deep learning has led to significant achievements in computer vision, primarily driven by complex convolutional neural networks (CNNs). However, the increasing depth and parameter count of these networks often result in overfitting and elevated computational demands. Knowledge distillation (KD) has emerged as a promising technique to address these issues by transferring knowledge from a large, well-trained teacher model to a more compact student model. This paper introduces a novel knowledge distillation method that simplifies the distillation process and narrows the performance gap between teacher and student models without relying on intricate knowledge representations. Our approach leverages a unique teacher network architecture designed to enhance the efficiency and effectiveness of knowledge transfer. Additionally, we introduce a streamlined teacher network architecture that transfers knowledge effectively through a simplified distillation process, enabling the student model to achieve high accuracy with reduced computational demands. Comprehensive experiments conducted on the CIFAR-10 dataset demonstrate that our proposed model achieves superior performance compared to traditional KD methods and established architectures such as ResNet and VGG networks. The proposed method not only maintains high accuracy but also significantly reduces training and validation losses. Key findings highlight the optimal hyperparameter settings (temperature T = 15.0 and smoothing factor α = 0.7), which yield the highest validation accuracy and lowest loss values. This research contributes to the theoretical and practical advancements in knowledge distillation, providing a robust framework for future applications and research in neural network compression and optimization. The simplicity and efficiency of our approach pave the way for more accessible and scalable solutions in deep learning model deployment.

Enabling On-Device Inference of Large Language Models : Challenges, Techniques, and Applications

This comprehensive article explores the cutting-edge techniques and challenges associated with on-device inference of Large Language Models (LLMs), a transformative approach that brings advanced AI capabilities directly to mobile and edge devices. The article delves into the intricate balance between the computational demands of LLMs and the resource constraints of mobile hardware, presenting a detailed analysis of various strategies to overcome these limitations. Key areas of focus include model compression techniques such as pruning and knowledge distillation, quantization methods, and the development of efficient model architectures. The article also examines the role of specialized hardware accelerators, including Neural Processing Units (NPUs), FPGAs, and ASICs, in enhancing on-device performance. Additionally, the article addresses critical aspects of memory management and optimization strategies crucial for efficient LLM deployment. Through a rigorous evaluation of performance metrics, the article offers insights into the trade-offs between model size, inference speed, and accuracy. It further explores diverse applications and use cases, from real-time language translation to privacy-preserving text analysis, highlighting the transformative potential of on-device LLM inference. The article concludes with an examination of ongoing challenges and future research directions, including improving energy efficiency, enhancing model adaptability, and addressing privacy and security concerns. This comprehensive article provides researchers, developers, and industry professionals with a thorough understanding of the current state and future prospects of on-device LLM inference, underlining its significance in shaping the next generation of AI-powered mobile and IoT applications.

A novel machine learning workflow to optimize cooling devices grounded in solid-state physics

Cooling devices grounded in solid-state physics are promising candidates for integrated-chip nanocooling applications. These devices are modeled by coupling the quantum non-equilibirum Green’s function for electrons with the heat equation (NEGF+H), which allows to accurately describe the energetic and thermal properties. We propose a novel machine learning (ML) workflow to accelerate the design optimization process of these cooling devices, alleviating the high computational demands of NEGF+H. This methodology, trained with NEGF+H data, obtains the optimum heterostructure designs that provide the best trade-off between the cooling power of the lattice (CP) and the electron temperature (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {\ ext{T}}_{e} $$\\end{document}). Using a vast search space of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.18 \ imes 10^{-5}$$\\end{document} different device configurations, we obtained a set of optimum devices with prediction relative errors lower than \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${4}\\,\\%$$\\end{document} for CP and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${1}\\,\\%$$\\end{document} for Te. The ML workflow reduces the computational resources needed, from two days for a single NEGF+H simulation to 10 s to find the optimum designs.

Exploring the integration of a global AI model with traditional data assimilation in weather forecasting

Abstract Recent advancements in artificial intelligence (AI) have profoundly transformed weather forecasting, challenging traditional reliance on numerical weather prediction (NWP) models. Despite notable progress, AI models still depend heavily on traditional NWP systems and data assimilation methods to generate analysis fields, a dependency that increases computational demands and might limit forecast accuracy. This study explored the integration of Gridpoint Statistical Interpolation (GSI) with the Pangu-Weather AI forecasting model (GSI-Pangu), and assessed the potential for AI models to autonomously generate forecasts by leveraging mature data assimilation systems. Our experiments commenced by adopting ERA5 reanalysis data for the initial cycle, and then involved assimilation of simulated observations in subsequent cycles, spanning a month-long period. Results demonstrated notable enhancements in forecast accuracy, with reductions in the root mean square error across various atmospheric variables compared with the results of a control experiment without data assimilation. Additionally, the results highlighted GSI-Pangu’s ability to predict large-scale circulation patterns of extreme precipitation events, together with its effectiveness in driving regional models to accurately forecast precipitation intensity and distribution. Successful implementation of GSI within the Pangu-Weather framework underscores the transformative potential of hybrid forecasting systems, which merge conventional meteorological techniques with AI innovations, thereby facilitating accelerated adoption of AI in weather forecasting.

Reliable Automated ECG Arrhythmia Classification Using Reinforced VGG‐27 Neural Network Framework

ABSTRACTAutomated categorization of electrocardiogram (ECG) waveforms using deep learning (DL) methods has garnered considerable attention in recent research. However, prevalent DL networks encounter challenges including overfitting, class imbalance, limitations in deeper network training, and high computational demands. To address these issues, this study proposes an Automated ECG Arrhythmia Classification framework employing the Reinforced Visual Geometry Group‐27 (REF‐VGG‐27). Initially, the framework encompasses preprocessing steps such as denoising, R‐peak identification, data balancing, and cross‐validation. For automatic feature extraction and classification, two DL architectures are suggested: a novel hybrid model combining 2D convolutional neural network (2DCNN) with VGG‐16, featuring a deep architecture for extracting morphological characteristics, frequency features related to heart rate variability (HRV), and statistical attributes crucial for identifying atrial fibrillation (AF). Subsequently, to classify arrhythmia patterns, the VGG‐16 Model is employed. Utilizing publicly available ECG image datasets, the proposed model achieved remarkable accuracy benchmarks: 99.61% accuracy, precision of 99.61%, and recall of 99.48%. Comparative analysis with existing approaches substantiates the efficiency and robustness of our model.

Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence

The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.

An engineering-oriented Shallow-water Hydro-Sediment-Morphodynamic model using the GPU-acceleration and the hybrid LTS/GMaTS method

Engineering applications of finite volume Shallow-water Hydro-Sediment-Morphodynamic models (SHSM) have faced limitations due to their high computational demands arising from either extremely large amounts of computational cells or extremely small time steps at some regions and simultaneously the adoption of the globally minimum time step. To this end, we present an engineering-oriented modeling framework by (1) using the GPU-acceleration that overcomes the challenge of extremely large amounts of computational cells and (2) using a hybrid local-time-stepping/global maximum time step (LTS/GMaTS) strategy that mitigates the extremely small local time steps necessitated by locally-refined meshes or non-uniformity of flow conditions. The GPU parallel algorithm is tailored to fully leverage the computational power of GPU, optimizing numerical structure, kernel functions and memory usage, all in conjunction with the hybrid LTS/GMaTS implementation. We demonstrate its computational efficiency by simulating one experimental dam-break flow and a field-scale case in the Xinjiu waterway, Middle Yangtze River. The results show that the scheme performs well in terms of accuracy, efficiency, and robustness in reproducing real-world hydro-sediment-morphological evolution.

A Precise and Scalable Indoor Positioning System Using Cross-Modal Knowledge Distillation.

User location has emerged as a pivotal factor in human-centered environments, driving applications like tracking, navigation, healthcare, and emergency response that align with Sustainable Development Goals (SDGs). However, accurate indoor localization remains challenging due to the limitations of GPS in indoor settings, where signal interference and reflections disrupt satellite connections. While Received Signal Strength Indicator (RSSI) methods are commonly employed, they are affected by environmental noise, multipath fading, and signal interference. Round-Trip Time (RTT)-based localization techniques provide a more resilient alternative but are not universally supported across access points due to infrastructure limitations. To address these challenges, we introduce DistilLoc: a cross-knowledge distillation framework that transfers knowledge from an RTT-based teacher model to an RSSI-based student model. By applying a teacher-student architecture, where the RTT model (teacher) trains the RSSI model (student), DistilLoc enhances RSSI-based localization with the accuracy and robustness of RTT without requiring RTT data during deployment. At the core of DistilLoc, the FNet architecture is employed for its computational efficiency and capacity to capture complex relationships among RSSI signals from multiple access points. This enables the student model to learn a robust mapping from RSSI measurements to precise location estimates, reducing computational demands while improving scalability. Evaluation in two cluttered indoor environments of varying sizes using Android devices and Google WiFi access points, DistilLoc achieved sub-meter localization accuracy, with median errors of 0.42 m and 0.32 m, respectively, demonstrating improvements of 267% over conventional RSSI methods and 496% over multilateration-based approaches. These results validate DistilLoc as a scalable, accurate solution for indoor localization, enabling intelligent, resource-efficient urban environments that contribute to SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities).

Comparative Evaluation of Sentiment Analysis Methods: From Traditional Techniques to Advanced Deep Learning Models

Abstract. Sentiment evaluation plays a crucial role in deciphering public perception and consumer responses in today's digital landscape. This investigation offers a thorough assessment of diverse sentiment evaluation techniques, contrasting conventional machine learning methodologies with cutting-edge deep learning frameworks. In particular, the research scrutinizes the efficacy of Bidirectional Encoder Representations from Transformers (BERT)-derived architectures (BERT-Base and Robustly Optimized BERT Pretraining Approach (RoBERTa)), Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Support Vector Machines (SVM), and Naive Bayes classifiers. The study gauges these approaches based on their precision, recall, F1-metric, overall accuracy, and computational efficiency using an extensive sentiment evaluation dataset. The results reveal that BERT-based models, particularly RoBERTa, achieve the highest accuracy (87.44%) and F1-score (0.8746), though they also require the longest training time (approximately 3 hours). CNN and LSTM models strike a balance between performance and efficiency, while traditional methods like SVM and Naive Bayes offer faster training and deployment with moderate accuracy. The insights gained from this study are valuable for both researchers and practitioners, highlighting the trade-offs between model performance, computational demands, and practical deployment considerations in sentiment analysis applications.

XEdgeAI: A human-centered industrial inspection framework with data-centric Explainable Edge AI approach

Recent advancements in deep learning have significantly improved visual quality inspection and predictive maintenance within industrial settings. However, deploying these technologies on low-resource edge devices poses substantial challenges due to their high computational demands and the inherent complexity of Explainable AI (XAI) methods. This paper addresses these challenges by introducing a novel XAI-integrated Visual Quality Inspection framework that optimizes the deployment of semantic segmentation models on low-resource edge devices. Our framework incorporates XAI and the Large Vision Language Model to deliver human-centered interpretability through visual and textual explanations to end-users. This is crucial for end-user trust and model interpretability. We outline a comprehensive methodology consisting of six fundamental modules: base model fine-tuning, XAI-based explanation generation, evaluation of XAI approaches, XAI-guided data augmentation, development of an edge-compatible model, and the generation of understandable visual and textual explanations. Through XAI-guided data augmentation, the enhanced model incorporating domain expert knowledge with visual and textual explanations is successfully deployed on mobile devices to support end-users in real-world scenarios. Experimental results showcase the effectiveness of the proposed framework, with the mobile model achieving competitive accuracy while significantly reducing model size. This approach paves the way for the broader adoption of reliable and interpretable AI tools in critical industrial applications, where decisions must be both rapid and justifiable. Our code for this work can be found at https://github.com/Analytics-Everywhere-Lab/vqixai.

Exploring the Impact of Word2Vec Embeddings Across Neural Network Architectures for Sentiment Analysis

Abstract. Sentiment analysis is crucial for understanding public opinion, gauging customer satisfaction, and making informed business decisions based on the emotional tone of textual data. This study investigates the performance of different Word2Vec-based embedding strategies static, non-static, and multichannel for sentiment analysis across various neural network architectures, including Convolution Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). Despite the rise of advanced contextual embedding methods such as Bidirectional Encoder Representations from Transformers (BERT), Word to Vector (Word2Vec) retains its importance due to its simplicity and lower computational demands, making it ideal for use in settings with limited resources. The goal is to evaluate the impact of fine-tuning Word2Vec embeddings on the accuracy of sentiment classification. Using the Internet Movie Database (IMDb), this work finds that multichannel embeddings, which combine static and non-static representations, provide the best performance across most architectures, while static embeddings continue to deliver strong results in specific sequential models. These findings highlight the balance between efficiency and accuracy in traditional word embeddings, particularly when advanced models are not feasible.

Lightweight remote sensing super-resolution with multi-scale graph attention network

Remote Sensing Super-Resolution (RS-SR) constitutes a pivotal component in the domain of remote sensing image analysis, aimed at enhancing the spatial resolution of low-resolution imagery. Recent advancements have seen deep learning techniques achieving substantial progress in the RS-SR field. Notably, Graph Neural Networks (GNNs) have emerged as a potent mechanism for processing remote sensing images, adept at elucidating the intricate inter-pixel relationships within images. Nevertheless, a prevalent limitation among existing GNN-based methodologies is their disregard for the high computational demands, which circumscribes their applicability in environments with limited computational resources. This paper introduces a streamlined RS-SR framework, leveraging a Multi-Scale Graph Attention Network (MSGAN), designed to effectively balance computational efficiency with high performance. The core of MSGAN is a novel multi-scale graph attention module, integrating graph attention block and multi-scale lattice block structures, engineered to comprehensively assimilate both localized and extensive spatial information in remote sensing images. This enhances the framework’s overall efficacy and resilience in RS-SR tasks. Comparative experimental analyses demonstrate that MSGAN delivers competitive results against state-of-the-art methods while reducing parameter count and computational overhead, presenting a promising avenue for deployment in scenarios with limited computational resources.

Exploring the Impact of Word2Vec Embeddings Across Neural Network Architectures for Sentiment Analysis

Abstract. Sentiment analysis is crucial for understanding public opinion, gauging customer satisfaction, and making informed business decisions based on the emotional tone of textual data. This study investigates the performance of different Word2Vec-based embedding strategies static, non-static, and multichannel for sentiment analysis across various neural network architectures, including Convolution Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). Despite the rise of advanced contextual embedding methods such as Bidirectional Encoder Representations from Transformers (BERT), Word to Vector (Word2Vec) retains its importance due to its simplicity and lower computational demands, making it ideal for use in settings with limited resources. The goal is to evaluate the impact of fine-tuning Word2Vec embeddings on the accuracy of sentiment classification. Using the Internet Movie Database (IMDb), this work finds that multichannel embeddings, which combine static and non-static representations, provide the best performance across most architectures, while static embeddings continue to deliver strong results in specific sequential models. These findings highlight the balance between efficiency and accuracy in traditional word embeddings, particularly when advanced models are not feasible.

Simplified Gravity Load Collapse Dynamic Analysis of Old-Type Reinforced Concrete Frames

The results of shaking table tests from previous studies on a one-story, two-bay reinforced concrete frame—exhibiting both shear and axial failures—were compared with nonlinear dynamic analyses using simplified models intended to evaluate the collapse potential of older reinforced concrete structures. To replicate the nonlinear behavior of columns, whether shear-critical or primarily flexure-dominant, a one-component beam model was applied. This model features a linear elastic element connected in series to a rigid plastic, linearly hardening spring at each end, representing a concentrated plasticity component. To account for strength degradation through path-dependent plasticity, a negative slope model as degradation was implemented, linking points at both shear and axial failure. The shear failure points were determined through pushover analysis of shear-critical columns using Phaethon software. Although the simplified model provided a reasonable approximation of the overall frame response and lateral strength degradation, especially in terms of drift, its reduced computational demands led to some discrepancies between the calculated and measured shear forces and drifts during certain segments of the time-history response.

Digital Twin-Driven Optimization Of Bioenergy Production From Waste Materials

This study explores the transformative role of digital twin technology in optimizing bioenergy production, focusing on its applications in real-time monitoring, process simulation, predictive maintenance, and hybrid energy systems. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a total of 94 peer-reviewed articles were systematically identified, screened, and analyzed to ensure a comprehensive and rigorous review. The findings highlight the advancements in digital twin technology, including its integration with IoT-enabled sensors and machine learning algorithms, which enable dynamic system optimization and predictive capabilities. The study also examines the role of digital twins in hybrid energy systems, demonstrating their potential to enhance energy efficiency by up to 30% through seamless integration with solar and wind energy. Despite these advancements, the review identifies critical challenges, such as high computational demands, data integration issues, and economic and policy barriers, which limit the scalability and widespread adoption of digital twins in industrial bioenergy applications. This study contributes to the growing body of knowledge by offering a comprehensive synthesis of existing research, identifying key gaps, and emphasizing the need for interdisciplinary collaboration and policy support to fully harness the potential of digital twins in achieving sustainable energy goal.

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses.

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.

Optical light scattering to improve image classification via wavelength division multiplexing

Machine learning is constantly contributing significant progress in many areas while posing huge demands for computing resources. It has been demonstrated the feasibility of leveraging random light scattering to decrease the computational resource demands of image classification algorithms. However, optical devices in optical random scattering systems, such as cameras, constrain the bandwidth of the entire system. In this study, a high-speed scattering system based on wavelength division multiplexing (WDM) was proposed. By employing the high bandwidth semiconductor lasers and quadrant PIN detectors, this WDM scattering system achieves over a 1000-times increase in acquisition speed compared to the traditional camera-based spatial scattering system. Moreover, this WDM scattering system has been demonstrated to improve the classification accuracy for RC on nine datasets, including MNIST, Chest_X-ray, and Malaria, by 26.15%.

A non-intrusive reduced-order model for finite element analysis of implant positioning in total hip replacements.

Sophisticated high-fidelity simulations can predict bone mass density (BMD) changes around a hip implant after implantation. However, these models currently have high computational demands, rendering them impractical for clinical settings. Model order reduction techniques offer a remedy by enabling fast evaluations. In this work, a non-intrusive reduced-order model, combining proper orthogonal decomposition with radial basis function interpolation (POD-RBF), is established to predict BMD distributions for varying implant positions. A parameterised finite element mesh is morphed using Laplace's equation, which eliminates tedious remeshing and projection of the BMD results on a common mesh in the offline stage. In the online stage, the surrogate model can predict BMD distributions for new implant positions and the results are visualised on the parameterised reference mesh. The computational time for evaluating the final BMD distribution around a new implant position is reduced from minutes to milliseconds by the surrogate model compared to the high-fidelity model. The snapshot data, the surrogate model parameters and the accuracy of the surrogate model are analysed. The presented non-intrusive surrogate model paves the way for on-the-fly evaluations in clinical practice, offering a promising tool for planning and monitoring of total hip replacements.

Learning to predict perceptual visibility of rendering deterioration in computer games

Contemporary computer gaming affords players the agency to manually tailor rendering settings, a capability crucial for optimizing computational demands following their hardware performance. Specifically, adjustments to texture resolution, shadow map intricacies, and anti-aliasing complexities facilitate seamless animation generation, particularly on systems equipped with budget-friendly graphical units. Nonetheless, the intricacy and extensive interdependencies among these rendering parameters render selecting an optimal configuration a multifaceted challenge. Our approach involves training a proprietary convolutional neural network (CNN) to streamline this intricate process. This CNN quantitatively compares reference images rendered at peak quality to their counterparts subjected to quality reduction. Its function involves identifying and classifying artifacts within the altered images, and evaluating their perceptibility to human observers. Our neural network undergoes rigorous training using an expansive dataset derived from prevalent game engine scenes. A meticulous process to establish accurate classification data involves manually annotating image regions afflicted by diminished quality. As a testament to our methodology, we implement a prototype forward renderer based on OpenGL. Employing the trained neural network within this application enables the evaluation of image quality across diverse anti-aliasing settings, culminating in identifying settings minimizing artifact visibility. Empirical validation via user studies substantiates our network’s efficacy, demonstrating its superior discernment of artifact visibility compared to established image quality metrics.

Applications and Challenges of AI in PCB X-ray Inspection: A Comprehensive Study

As printed circuit boards (PCBs) continue to evolve in complexity and miniaturization, the demand for robust and efficient inspection techniques has become paramount in ensuring the quality and reliability of electronic devices. The application of machine learning and deep learning techniques has revolutionized PCB inspection in recent years, enabling the ability to automate and improve numerous elements of the process. In this paper, a comprehensive analysis is performed on the applications and challenges of artificial intelligence (AI), encompassing techniques of deep learning and machine learning, in the domain of PCB X-ray scrutiny. The main focus of this research centers around defect detection, identification of components and layers, deep learning algorithms for image reconstruction, as well as the identification of defects and features in advanced packaging. This study examines the current cutting-edge advancements in each of these areas, closely examining the existing methodologies and technologies employed. Furthermore, it delves into the limitations and challenges inherent in PCB X-ray inspection, such as the unavailability of data, computational demands, and the interpretability of models. In addition, this article offers prospective insights and presents promising avenues like application of generative adversarial networks and deep learning reconstruction methods for future exploration.