Advances In Hardware Research Articles

Transforming drug discovery: the impact of AI and molecular simulation on R&D efficiency.

The process of developing new drugs in the pharmaceutical industry is both time-consuming and costly, making efficiency crucial. Recent advances in hardware and computational methods have led to the widespread application of computational science approaches in drug discovery. These approaches, including artificial intelligence and molecular simulations, span from target identification to pharmacokinetics research, aiming to reduce the likelihood of failure and present lower costs. Machine learning-based methods predict new applications for developing new drugs based on accumulated knowledge, while molecular simulations estimate interactions between drugs and target proteins at the atomic level based on physical laws. Each approach has its advantages and disadvantages, and they complement each other. As a result, the future of computational science approaches in drug discovery is expected to focus on developing new methodologies that integrate these two techniques to enhance the efficiency of drug discovery.

Self‐Rectifying Switching Memory Based on HfOx/FeOx Semiconductor Heterostructure for Neuromorphic Computing

AbstractSneak‐path current is one of the biggest barriers for large‐scale passive memristor array integration. An ideal self‐rectifying resistance random access memory (SR‐RRAM) is a desirable solution but it has not been demonstrated today for optimizing comprehensive indexes for neuromorphic computing. The HfOx/FeOx semiconductor heterojunction SR‐RRAM with a robust self‐rectifying switching behavior featured by an average rectifying ratio (≈104), high resistance ratio (>106), high cycling endurance (>104 cycles), high computing precision (>6 bits) and synaptic plasticity such as paired‐pulse facilitation (PPF) and the spike‐timing‐dependent plasticity (STDP) for artificial intelligence recognition is developed using the unidirectional conductivity feature of p‐n junction. The electron hopping, tunneling, and blocking in this semiconductor heterojunction that is verified by the energy band mode based on UV photoelectron spectroscopy (UPS) technology and low‐energy inverse photoelectron spectroscopy (LEIPS) and in situ high resolution transmission electron microscopy (HR‐TEM) observation plays a dominant role in the self‐rectifying analog switching behaviors. This work provides energy‐band engineering for the large‐scale memristor array integration, representing a significant advancement in hardware for neuromorphic computing.

Evolving the Cloud Block Store with Performance, Elasticity, Availability, and Hardware Offloading

In this paper, we qualitatively and quantitatively discuss the design choices, production experience, and lessons in building the Elastic Block Storage ( EBS ) at Alibaba Cloud over the past decade. To cope with hardware advancement and users’ demands, we shift our focus from design simplicity in EBS1 to high performance and space efficiency in EBS2 , and finally reducing network traffic amplification in EBS3 . In addition to the architectural evolutions, we also summarize development lessons and experiences as four topics, including: (i) achieving high elasticity in latency, throughput, IOPS and capacity; (ii) improving availability by minimizing the blast radius of individual, regional, and global failure events; (iii) identifying the motivations and key tradeoffs in various hardware offloading solutions; and (iv) identifying the pros/cons of alternative solutions and explaining why seemingly promising ideas would not work in practice.

Survey Paper on Virtual Reality in Gaming

Abstract: This paper examines the integration of virtual reality (VR) technology within gaming, focusing on the development of immersive virtual environments that enhance user interaction, presence, and satisfaction. With advancements in VR hardware and software, gaming experiences are becoming more lifelike and socially engaging. The VR-based metaverse concept explored in this research aims to provide interconnected virtual worlds with dynamic, persistent interactions. This paper discusses the benefits and challenges of VR in gaming, including technical limitations, high costs, and potential for immersive storytelling. The findings offer insights into the potential impact of VR in gaming and entertainment

Blockchain-inspired intelligent framework for logistic theft control

The smart logistics industry utilizes advanced software and hardware technologies to enhance efficient transmission. By integrating smart components, it identifies vulnerabilities within the logistics sector, making it more susceptible to physical attacks aimed at theft and control. The main goal is to propose an effective logistics monitoring system that automates theft prevention. Specifically, the suggested model analyzes logistics transmission patterns through secure surveillance enabled by IoT-based blockchain technology. Additionally, a bi-directional convolutional neural network is employed to evaluate real-time theft vulnerabilities, aiding optimal decision-making. The proposed method has been shown to provide accurate real-time analysis of risky behaviors. Experimental simulations indicate that the proposed solution significantly improves logistics monitoring. The system’s performance is assessed using various statistical metrics, including latency rate (7.44 s), a data processing cost (O((n−1)logn)), and model training and testing results (precision (94.60%), recall (95.67%), and F-Measure (96.64%)), statistical performance (error reduction (48%)) and reliability (94.48%).

Development And Testing of Arduino Based Autonomous Environmental Monitoring System

This paper presents the development of an Autonomous Environmental Monitoring System (AEMS) designed to track various environmental parameters. With the growing demand for autonomous monitoring systems in today’s modern era, we propose an Arduino-based real-time hardware solution to measure parameters such as flame, temperature, humidity, and distance. The system leverages advanced and cost-effective electronic sensors and hardware to monitor these environmental factors. We developed individual sensor modules for each parameter and integrated them into a unified multi-sensor system through sensor fusion. The collected data is then displayed on a 16 × 2 LCD module. The result is a fully functional autonomous monitoring system, tested under laboratory environmental conditions using a compact, handheld device.

Means and Methods of Collecting Indicators for Energy Supply Companies

This study provides a comprehensive overview of the various means and methods employed in gathering data, emphasizing the need for advanced technologies in the face of increasing energy demands and evolving regulatory environments. A thorough comparative analysis focuses on several key aspects, including technology comparison, data accuracy and reliability, real-time data collection capabilities, cost effectiveness, scalability, and flexibility, consumer interaction, and feedback mecha- nisms. Particular attention has been given to the security and confidentiality of data, as well as the environmental implications. The analysis extends to explore hardware and technological advancements in the industry, comparing traditional systems with modern automated and digital solutions, such as smart meters and integrated data management platforms.

Comparison Distributed and Parallel Machine Learning: Evidence from Models, Principles and Application Scenarios

Abstract. Contemporarily, the demand for processing large-scale data has been rapidly increasing, prompting continuous advancements in the field of machine learning. This study examines the state of parallel and distributed machine learning at present, both of which aim to enhance computational efficiency when dealing with large datasets and demonstrate promising applications across various domains. As the scale of data escalates, traditional single-machine learning methods are becoming increasingly inadequate, which lead to the emergence of parallel and distributed machine learning. These approaches enable substantial computations to be performed more efficiently through the collaboration of multi-core CPUs or multiple computing nodes. This research conducts an in-depth analysis of the inherent challenges associated with these methods, including data transmission latency, synchronization requirements, and user privacy concerns. Ultimately, this research emphasizes the tremendous potential of both methods for future applications, a potential that is bolstered by ongoing advancements in hardware and algorithm optimization. These results provide valuable insights for practitioners in the field and offers guidance for future research directions.

Seesaw: A 4096-bit vector processor for accelerating Kyber based on RISC-V ISA extensions

The ML-KEM standard based on Kyber algorithm is one of the post-quantum cryptography (PQC) standards released by the National Institute of Standards and Technology (NIST) to withstand quantum attacks. To increase throughput and reduce the execution time that is limited by the high computational complexity of the Kyber algorithm, an RISC-V-based processor Seesaw is designed to accelerate the Kyber algorithm. The 32 specialized extension instructions are mainly designed to enhance the parallel computing ability of the processor and accelerate all the processes of the Kyber algorithm by thoroughly analyzing its characteristics. Subsequently, by carefully designing hardware such as poly vector registers and algorithm execution units on the RISC-V processor, the support of microarchitecture for extension instructions was achieved. Seesaw supports 4096-bit vector calculations through its poly vector registers and execution unit to meet high-throughput requirements and is implemented on the field-programmable gate array (FPGA). In addition, we modify the compiler simultaneously to adapt to the instruction extension and execution of Seesaw. Experimental results indicate that the processor achieves a speed-up of 432× and 18864× for hash and NTT, respectively, compared with that without extension instructions and a speed-up of 5.6× for the execution of the Kyber algorithm compared with the advanced hardware design.

Insights into the Structure and Dynamics of Proteins from 19F Solution NMR Spectroscopy.

19F NMR spectroscopy has recently witnessed a resurgence as an attractive analytical tool for the study of the structure and dynamics of biomolecules in vitro and in cells, despite reports of its applications in biomolecular NMR since the 1970s. The high gyromagnetic ratio, large chemical shift dispersion, and complete absence of the spin 1/2 19F nucleus from biomolecules results in background-free, high-resolution 19F NMR spectra. The introduction of 19F probes in a few selected locations in biomolecules reduces spectral crowding despite its increased line width in comparison to typical 1H NMR line widths and allows rapid site-specific measurements from simple 1D spectra alone. The design and synthesis of novel 19F probes with reduced line widths and increased chemical shift sensitivity to the surrounding environment, together with advances in labeling techniques, NMR methodology, and hardware, have overcome several drawbacks of 19F NMR spectroscopy. The increased interest and widespread use of 19F NMR spectroscopy of biomolecules is gradually establishing it as a sensitive and high-resolution probe of biomolecular structure and dynamics, supplementing traditional 13C/15N-based methods. This Review focuses on the advances in 19F solution NMR spectroscopy of proteins in the past 5 years, with an emphasis on novel 19F tags and labeling techniques, NMR experiments to probe protein structure and conformational dynamics in vitro, and in-cell NMR applications.

Scalable Fabrication of Neuromorphic Devices Using Inkjet Printing for the Deposition of Organic Mixed Ionic‐Electronic Conductor

AbstractRecent advancements in artificial intelligence (AI) have highlighted the critical need for energy‐efficient hardware solutions, especially in edge‐computing applications. However, traditional AI approaches are plagued by significant power consumption. In response, researchers have turned to biomimetic strategies, drawing inspiration from the ion‐mediated operating principle of biological synapses, to develop organic neuromorphic devices as promising alternatives. Organic mixed ionic‐electronic conductor (OMIEC) materials have emerged as particularly noteworthy in this field, due to their potential for enhancing neuromorphic computing capabilities. Together with device performance, it is crucial to select devices that allow fabrication via scalable techniques. This study investigates the fabrication of OMIEC‐based neuromorphic devices using inkjet printing, providing a scalable and material‐efficient approach. Employing a commercially available polymer mixed ionic‐electronic conductor (BTEM‐PPV) and a lithium salt, inkjet‐printed devices exhibit performance comparable to those fabricated via traditional spin‐coating methods. These two‐terminal neuromorphic devices demonstrate functionality analogous to literature‐known devices and demonstrate promising frequency‐dependent short‐term plasticity. Furthermore, comparative studies with previous light‐emitting electrochemical cells (LECs) and neuromorphic OMIEC devices validate the efficacy of inkjet printing as a potential fabrication technique. The findings suggest that inkjet printing is suitable for large‐scale production, offering reproducible and stable fabrication processes. By adopting the OMIEC material system, inkjet printing holds the potential for further enhancing device performance and functionality. Overall, this study underscores the viability of inkjet printing as a scalable fabrication method for OMIEC‐based neuromorphic devices, paving the way for advancements in AI hardware.

A meta-learning based approach for temporal link prediction in multiplex networks

Link prediction in temporal and multiplex networks is a crucial issue across both applied and scientific disciplines within the study of complex networks. Recent advances in hardware and the increased availability of computational resources have enhanced our capacity to tackle this problem more effectively.Link prediction in multiplex and temporal networks faces challenges such as inter-layer dependencies and the temporal expansion and contraction of the network. This paper introduces MetaLink, a novel approach designed for link prediction within such temporal multiplex networks. MetaLink leverages knowledge obtained from various temporal network snapshots by employing two innovative methods for subsequent temporal snapshot. It efficiently facilitates the transfer of knowledge across different temporal snapshots. The intra-layer knowledge transfer is governed by a time-decay function, while inter-layer knowledge is learned in a step wise and transferred using the Model-Agnostic Meta-Learning (MAML) algorithm from one snapshot (task) to another. Our findings demonstrate that MetaLink significantly outperforms static single-layer and multiplex methods, showing improvements of 2 to 5 percent, and exhibits up to a 3 percent enhancement over existing temporal methods.

129Xe Image Processing Pipeline: An open-source, graphical user interface application for the analysis of hyperpolarized 129Xe MRI.

Hyperpolarized 129Xe MRI presents opportunities to assess regional pulmonary microstructure and function. Ongoing advancements in hardware, sequences, and image processing have helped it become increasingly adopted for both research and clinical use. As the number of applications and users increase, standardization becomes crucial. To that end, this study developed an executable, open-source 129Xe image processing pipeline (XIPline) to provide a user-friendly, graphical user interface-based analysis pipeline to analyze and visualize 129Xe MR data, including scanner calibration, ventilation, diffusion-weighted, and gas exchange images. The customizable XIPline is designed in MATLAB to analyze data from all three major scanner platforms. Calibration data is processed to calculate optimal flip angle and determine129Xe frequency offset. Data processing includes loading, reconstructing, registering, segmenting, and post-processing images. Ventilation analysis incorporates three common algorithms to calculate ventilation defect percentage and novel techniques to assess defect distribution and ventilation texture. Diffusion analysis features ADC mapping, modified linear binning to account for ADC age-dependence, and common diffusion morphometry methods. Gas exchange processing uses a generalized linear binning for data acquired using 1-point Dixon imaging. The XIPline workflow is demonstrated using analysis from representative calibration, ventilation, diffusion, and gas exchange data. The application will reduce redundant effort when implementing new techniques across research sites by providing an open-source framework for developers. In its current form, it offers a robust and adaptable platform for 129Xe MRI analysis to ensure methodological consistency, transparency, and support for collaborative research across multiple sites and MRI manufacturers.

Dual-energy computed tomography: pediatric considerations.

Multidetector computed tomography (CT) has revolutionized medicine and is now a fundamental aspect of modern radiology. Hardware and software advancements have significantly improved CT accessibility, image quality, and acquisition times. While considerable attention has been directed towards the potential risks of ionizing radiation from CT scans in children, recent concerns regarding the possible short- and long-term risks related to magnetic resonance imaging (MRI) conducted under general anesthesia have generated fresh interest in novel pediatric CT applications and techniques that allow imaging of awake patients at low radiation doses. Among these novel techniques, dual-energy CT (DECT) stands out for its ability to provide enhanced diagnostic information, reduce radiation doses further, and facilitate faster scans, making it a highly promising tool in pediatric radiology. This manuscript explores the current role of DECT in pediatric imaging, emphasizing its technical foundations, hardware configurations, and various reconstruction techniques. We discuss advanced post-processing techniques, such as material decomposition algorithms and virtual monoenergetic imaging, highlighting their clinical advantages in improving diagnostic accuracy and patient outcomes. Furthermore, the paper reviews the clinical applications of DECT in evaluating pulmonary perfusion, cardiovascular assessments, and oncologic imaging in pediatric patients.

Emerging Technologies and Applications of AI Chips: Integrating Deep Learning Algorithms with Advanced Hardware Architectures

Due to the ongoing promotion of the latest technological revolution and industrial upgrading, as well as the widespread use of artificial intelligence and deep learning, the intelligent Internet of Things terminal has reached a more advanced stage of development. The combination of training data, model algorithms, and processing power is the basis for advancing artificial intelligence. The semiconductor business plays a crucial role in shaping the future of AI algorithms and the computing power industry. This study explores the progression of artificial intelligence, and essential technologies, and examines the categorization and structure of AI chips. The process of deep learning was then analyzed, demonstrating the application of AI in real life. Next, this study showcases distinct AI chip-related models, specifically designed for the automated implementation of Convolutional Neural Networks (CNNs) on Field-Programmable Gate Arrays (FPGAs). The study then proceeds to examine the unique procedure and the significance of the resulting data. The future of AI was examined in the domains of space exploration, healthcare, and autonomous driving, along with future forecasts.

Quantifying the Impact of Hair and Skin Characteristics on Signal Quality with Practical Recommendations for Improvement.

Functional Near-Infrared Spectroscopy (fNIRS) holds transformative potential for research and clinical applications in neuroscience due to its non-invasive nature and adaptability to real-world settings. However, despite its promise, fNIRS signal quality is sensitive to individual differences in biophysical factors such as hair and skin characteristics, which can significantly impact the absorption and scattering of near-infrared light. If not properly addressed, these factors risk biasing fNIRS research by disproportionately affecting signal quality across diverse populations. Our results quantify the impact of various hair properties, skin pigmentation as well as head size, sex and age on signal quality, providing quantitative guidance for future hardware advances and methodological standards to help overcome these critical barriers to inclusivity in fNIRS studies. We provide actionable guidelines for fNIRS researchers, including a suggested metadata table and recommendations for cap and optode configurations, hair management techniques, and strategies to optimize data collection across varied participants. This research paves the way for the development of more inclusive fNIRS technologies, fostering broader applicability and improved interpretability of neuroimaging data in diverse populations.

ASIP Performance Enhancement by Hazard Control through Scoreboard.

The application-specific instruction set processor (ASIP) has been gradually accepted in AI, communication, media, game and industry control. The digital signal processor (DSP) is a typical ASIP, whose benefits include high performance in specific domains, low power consumption, high flexibility and low silicon consumption. One of the challenges for DSP design is to handle problems induced by datapath acceleration. The datapath acceleration (instruction fusion, black box instructions) induces control complexities. To most efficiently utilize hardware, control challenges can be summarized as RAW (Read-After-Write) handling, hardware hazard handling, and WAW (Write-After-Write) handling. Both an advanced compiler and hardware hazard handler can be used as solutions. In this paper, we introduced both solutions and exposed the benefits from the hardware solution. The benefits include utilizing low silicon to achieve higher performance and program memory reduction on chip. In summary, our solution only uses 0.91% extra silicon area yet achieves 32.75% performance improvement. So, the overall performance-to-cost ratio could be evaluated as 32%.

Multi-objective optimization algorithms for building performance assessment – A benchmark

The increasing development in the computational field that allowed software and hardware advances enables more refined building performance analysis. Simulation-based optimization (SBO) methods allow high standards to be achieved by combining parametric modeling, simulation, and optimization methods. However, SBO methods still need development, especially regarding the correct choice of the optimization algorithm based on the specific characteristics of each problem. This study proposes a multi-objective optimization algorithms benchmark by comparing seven multi-objective optimization algorithms: RBFMOpt, NSGA2, MHACO, NSPSO, MOEA/D, HypE, and SPEA2, across nine building-related problems, including thermal, energy, and daylight simulation. The problems varied from 5 to 18 discrete, continuous, and mixed parameters. The objective functions varied between two and three. We used the hypervolume indicator, IGD+, GD+, and EPS + to compare algorithms’ performance and assess the tendency to reduce computational costs. We also performed the Kruskal-Wallis non-parametric test to analyze the impact of multiple runs on the hypervolume indicator. The results showed that RBFMOpt and HypE perform best across all problems. However, RBFMOpt tends to reduce computational costs since the algorithm requires fewer simulations to obtain the best results.

Exploring AES Encryption Implementation Through Quantum Computing Techniques

A coming great revolution in technology is quantum computing, which opens new attacks on most of the developed cryptographic algorithms, including AES. These emerging quantum capabilities risk weakening cryptographic techniques, which safeguard a vast amount of data across the globe. This research uses Grover's algorithm to explore the vulnerabilities of the Advanced Encryption Standard to quantum attacks. By implementing quantum cryptographic algorithms and Quantum Error Correction on simulators and quantum hardware, the study evaluates the effectiveness of these techniques in mitigating noise and improving the reliability of quantum computations. The study shows that while AES is theoretically at risk due to Grover’s algorithm, which demonstrates a theoretical reduction in AES key search complexity, current hardware limitations and noise levels encountered in today’s quantum computers reduce the immediate threat and limit practical exploitation. The research also examines NTRU encryption, a quantum-resistant alternative, highlighting its robustness in quantum environments. The findings emphasize the need for further development in QEC and quantum-resistant cryptography to secure digital communications against future quantum threats. Future work will focus on advancing QEC techniques and refining quantum algorithms, addressing both hardware and theoretical advancements, including the potential use of high-capacity processors like Jiuzhang 3.0. These improvements will ensure the scalability of quantum-resistant systems to practical key sizes and usage scenarios.

Recovering MRI magnetic field strength properties using machine learning based on image compression quality scores

AbstractOver the years, significant hardware advancements have led to higher magnetic field MRI (Magnetic Resonance Imaging) acquisitions, providing improved diagnostic image quality at a higher cost. While higher image quality is desired, the investment required for high-field MRI imaging systems remains a significant consideration. Furthermore, historical patient records may still contain low-field magnetic strength MRI data. In this work, we present how image compression quality scores of MRI images could be used to recover their magnetic field strength properties. This work presents an interesting inverse problem scenario where output properties are used to recover an input property of an image. We implemented supervised machine learning models that utilize compressed image quality scores - based on Mean Squared Error (MSE), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF), and Earth Movers Distance (EM) metrics - as inputs. These models classify images into specific classes of magnetic field strength at which they were acquired (Low, Medium, and High) based solely on the quality scores of their compressed copies. The trained machine learning models (Support Vector Machine (SVM), Logistic Regression, K-nearest Neighbours (KNN), Decision Tree and Random Forest) achieved nearly 100% performance efficiency based on accuracy and F-1 score for all considered quality measures. This comprehensive evaluation offers insights into how compression techniques and quality factors impact image fidelity across various MRI magnetic field strengths. The findings of this study may prove valuable in recovering missing meta-tag information in historical image DICOM records and Context-Based Retrieval Systems (CBRS). The approach of using quality scores as features in training machine learning models, as demonstrated in this work, can be extended to other image groups where input parameters create statistically significant distinctions or are challenging to extract from images.