Hardware Configuration Research Articles

Heterogeneous Collaborative Learning for Personalized Healthcare Analytics Via Messenger Distillation.

The Healthcare Internet-of-Things (IoT) framework aims to provide personalized medical services with edge devices. Due to the inevitable data sparsity on an individual device, cross-device collaboration is introduced to enhance the power of distributed artificial intelligence. Conventional collaborative learning protocols (e.g., sharing model parameters or gradients) strictly require the homogeneity of all participant models. However, real-life end devices have various hardware configurations (e.g., compute resources), leading to heterogeneous on-device models with different architectures. Moreover, clients (i.e., end devices) may participate in the collaborative learning process at different times. In this paper, we propose a Similarity-Quality-based Messenger Distillation (SQMD) framework for heterogeneous asynchronous on-device healthcare analytics. By introducing a preloaded reference dataset, SQMD enables all participant devices to distill knowledge from peers via messengers (i.e., the soft labels of the reference dataset generated by clients) without assuming the same model architecture. Furthermore, the messengers also carry important auxiliary information to calculate the similarity between clients and evaluate the quality of each client model, based on which the central server creates and maintains a dynamic collaboration graph (communication graph) to improve the personalization and reliability of SQMD under asynchronous conditions. Extensive experiments on three real-life datasets show that SQMD achieves superior performance.

Improved path following for autonomous marine vehicles with low-cost heading/course sensors: comparative experiments

By overcoming constraints of low-cost sensors equipped on the autonomous marine vehicle (AMV), the classic path following approaches based on heading autopilot and course autopilot are improved to enhance the following accuracy of AMV, respectively. For the heading autopilot, adaptive line-of-sight (ALOS) is proposed to compensate for the generalized sideslip including the actual sideslip angle and the magnetic compass bias. For the course autopilot, extended Kalman filter (EKF) is employed to fusion the position and yaw angular rate to obtain the more precise and stable course, so as to substitute the unreliable course feedback from the satellite navigation receiver in cases of low speed and non ideal positioning. The performance and characteristics of both improved schemes are summarized through comparative experiments conducted on a low-cost AMV. Compared with the classic path following method, the straight line path following accuracy of the heading autopilot with generalized sideslip compensation and the course autopilot with EKF feedback are improved by 78.3% and 85.8% respectively. The circular path following accuracy are improved by 61.8% and 66.7% respectively. Engineers can choose the path following methods flexibly based on the hardware configuration, working environment and path type of the AMV to improve the following accuracy.

Comparison Study On Pinch-Hitting Vibration Signal Analysis for Automotive Bearing: I-KazTM and I-Kaz 3D

Rotating machines are now an essential part of the automotive industry. Meanwhile, a bearing is playing the most important component of rotating machinery. To sustain the system's smooth running, maintenance methods such as preventive maintenance, breakdown maintenance, and predictive maintenance are used. Under preventive maintenance, vibration analysis is used to diagnose machines bearing faults. The main objective is to recognize bearing defects in a mechanical device by acquiring signals from the bearing using data acquisition hardware. This analysis is conducted under various load torque conditions, speeds, and defect types. A modular hardware configuration consisting of an accelerometer acquires the vibration signal. The signals are analyzed by using I-kazTM and I-kaz 3D signal analysis and its main objective is to observe the degree of dispersion data from its mean point. This analysis resolves the issues associated with time domain analysis. This pinch-hitting analysis research was conducted in two stages. The first stage is an experimental process that uses 3 types of bearings, the healthy (BL), inner race fault (IRF), and defect at outer race (ORF) bearing on the Machine Fault Simulator and forces with a different type of speed (1000, 1500 and 2500 rpm) and load variation (0.0564, 0.564 and 1.1298 N-m). In the second stage, computing the coefficient value and plots of signal’s I-kazTM and I-kaz 3D based on the bearings type were done accordingly. As a result, the analysis for detecting inner race fault, the deviation percentage averages calculation obtained the I-kazTM coefficient shows a better result with 96.86% by comparing to the I-kaz 3D that achieves 94.20%. Similarly, for the outer race defect, I-kazTM lead with 65.40% compared to I-kaz 3D with only 54.82%.

Design and Build a Website-Based Teacher Payroll Processing Information System at SMKTIK Yadika Cicalengka

The design of this teacher payroll processing information system was developed to make it easier to manage teacher salaries at SMKTIK Yadika Cicalengka so as to minimize duplication.for the system design using the OOAD (object Oriented Analysis and Design) while the model used is RUP (Rational Unified Process) where the development tools are UML (Unified Modeling Language) There is also an explanation of the information system design process. Website-based payroll processing that will be built, starting from the design of use case diagrams, activity diagrams, sequence diagrams, class diagrams, user interfaces, hardware design and in designing this hardware requires hardware configuration, hardware specifications, and software specifications

Real-time navigation solutions of low-cost off-the-shelf GNSS receivers on board the Astrocast constellation satellites

With the ever-increasing number of small satellites launched into space, on-board orbit determination is becoming more and more important. Precise satellite positions are needed for constellation maintenance, formation flying, communication optimization, collision avoidance and Earth observation. In addition, accurate time information is required for the functioning of the instruments on board. Low-cost off-the-shelf Global Navigation Satellite System (GNSS) receivers and antennas, adapted to the small size and weight of the satellite, allow precise on-board orbit determination and time synchronization for even the smallest satellites.We analysed more than one year of on-board navigation solutions (NAVSOL) from 8 Astrocast CubeSats, which are equipped with our multi-GNSS single-frequency payload. Based on the residuals from a dynamic orbit fitting, we found that the quality of the NAVSOL from the Astrocast satellites varies strongly both over time and between satellites, with an RMS ranging from a few meters to several tens of meters.In this study, we focus on three main effects that influence the quality of the NAVSOL from the Astrocast satellites: (1) The orbit-fit residuals show a positive radial offset in the range of about 6 m to 15 m, which is caused by ionospheric refraction. Our data show that GNSS signals at negative elevations are very strongly affected by the ionospheric refraction and thus account for the major part of the radial offset. (2) Discrepancies between the NAVSOL positions and velocities, and simulations with a GNSS signal generator show a once-per-revolution periodicity in the out-of-plane component, with amplitudes of 2 m and 5 cm/s in position and velocity, respectively. This effect is probably due to an inappropriate dynamic model used in the real-time navigation algorithm of the GNSS receiver. (3) Depending on the location of the GNSS antenna on board the satellite, the noise level varies by a multiple. This is assumed to be due to the antenna orientations, interference of the GNSS signals with electromagnetic waves coming from the satellite platform, signal attenuation during the transmission from the antenna to the receiver and differences in the soldering or tuning of the individual GNSS antennas.These findings help us refine hardware configurations and firmware settings in preparation for future Astrocast satellites or other nanosatellite missions. By eliminating or minimizing the mentioned effects, we expect to consistently achieve an accuracy on the level of a few meters for the real-time navigation solution of commercial off-the-shelf single-frequency GNSS receivers in space.

The effect of 3D stereopsis and hand-tool alignment on learning effectiveness and skill transfer of a VR-based simulator for dental training.

Recent years have seen the proliferation of VR-based dental simulators using a wide variety of different VR configurations with varying degrees of realism. Important aspects distinguishing VR hardware configurations are 3D stereoscopic rendering and visual alignment of the user's hands with the virtual tools. New dental simulators are often evaluated without analysing the impact of these simulation aspects. In this paper, we seek to determine the impact of 3D stereoscopic rendering and of hand-tool alignment on the teaching effectiveness and skill assessment accuracy of a VR dental simulator. We developed a bimanual simulator using an HMD and two haptic devices that provides an immersive environment with both 3D stereoscopic rendering and hand-tool alignment. We then independently controlled for each of the two aspects of the simulation. We trained four groups of students in root canal access opening using the simulator and measured the virtual and real learning gains. We quantified the real learning gains by pre- and post-testing using realistic plastic teeth and the virtual learning gains by scoring the training outcomes inside the simulator. We developed a scoring metric to automatically score the training outcomes that strongly correlates with experts' scoring of those outcomes. We found that hand-tool alignment has a positive impact on virtual and real learning gains, and improves the accuracy of skill assessment. We found that stereoscopic 3D had a negative impact on virtual and real learning gains, however it improves the accuracy of skill assessment. This finding is counter-intuitive, and we found eye-tooth distance to be a confounding variable of stereoscopic 3D, as it was significantly lower for the monoscopic 3D condition and negatively correlates with real learning gain. The results of our study provide valuable information for the future design of dental simulators, as well as simulators for other high-precision psycho-motor tasks.

Wireless water consumption sensing system for building energy efficiency: A visual-based approach with self-powered operation

The use of smart wireless remote water meters has become popular due to their efficiency in avoiding manual labor and automate meter reading. The current implementation of these measurement instruments often requires expensive external power sources and separate data aggregators, posing challenges for circuit safety and the goal of reducing carbon emissions in aging communities. In response, a novel wireless system has been developed to accurately measure water consumption in buildings. This system incorporates advanced machine learning techniques and utilizes the potential of water-based energy in a seamless manner. This novel methodology utilizes optical sensors to acquire visual data of water gauges and exploits machine learning techniques, specifically the AVSM model also called Advanced Visual Sensing Model (AVSM). This methodology employs a rigorous approach to achieve accurate metric determinations, thereby eliminating the necessity for additional hardware or excessive power configurations. Moreover, an integrated micro water generator has been seamlessly implemented, demonstrating the capability to effectively transfer energy to a lithium battery for the purpose of storage. This implementation guarantees consistent functionality and a significant decrease in reliance on external power supplies. The data augmentation procedure involves the stochastic manipulation of image luminance, resulting in encouraging results when used in conjunction with the AVSM model. Significantly, the dimensions of the model have been significantly decreased, while maintaining a commendable mean average precision (mAP). By leveraging this method and visual-based features, the system can contribute to overall building energy efficiency and reduce the burdens of manual meter reading while supporting CO2 emissions reduction as a feasible and cost-effective solution for building management, promoting sustainability, and advancing energy-efficient practices in old communities. This new wireless system for measuring water usage provides a practical and affordable way to enhance energy efficiency in buildings, lessen the workload of manual meter reading, and help meet carbon emissions reduction goals. As well as optimizing resource and energy use, this method also aligns with eco-friendly solutions to combat global warming and climate change.

On the Performance Intricacies of Persistent Memory Aware Storage Engines

As key components of DBMSs, various storage engines and index structures have been proposed based on incorrect assumptions before PMem hardware is publicly available. Recent studies reveal that there is a significant performance gap in evaluating index structures on real PMem platforms as compared to DRAM-based emulators. However, a comprehensive evaluation for those PMem-aware database storage engines on real PMem hardware is still missing. Meanwhile, dynamic memory management is more important on PMem systems because PMem is slower than DRAM and unfriendly to random small-writes, and ensuring crash-consistency for the metadata of PMem allocators introduces extra overhead. Therefore, it is essential to understand the performance intricacies of PMem-aware database storage engines from the perspective of PMem allocators. This paper presents a systematic evaluation of three PMem-aware database storage engines using representative workloads and a unified benchmarking framework that is integrated with four PMem allocators. Besides the commonly used metrics, the impact of different hardware configurations (such as NUMA and eADR) on performance is also considered. Through in-depth analysis, we reveal caveats and pitfalls on using or designing PMem-aware storage engines and important insights that can serve as guidelines for future development of PMem allocators and other related components.

A Function-Based Approach to Interactive High-Precision Volumetric Design and Fabrication

We present a novel function representation (F-Rep) based geometric modeling kernel tailor-made to support computer aided design (CAD) and fabrication of high resolution volumetric models containing hundreds of billions of voxel grid elements. Our modeling kernel addresses existing limitations associated with evaluating, storing, and accessing volumetric data produced by F-Reps in contexts outside of rendering. The result is an F-Rep modeling kernel well suited for CAD-based applications. Our kernel utilizes a sparse volume data structure to manage F-Rep data while efficient F-Rep evaluation is achieved through a combination of interval arithmetic (IA), just-in-time (JIT) compilation of user-defined functions, and massively parallel evaluation on the GPU. We employ IA as the basis for local pruning of the function evaluation tree to minimize total function evaluations, we use a novel JIT compilation scheme to optimize function execution, and we take advantage of GPU-parallelism to enhance computational throughput. We illustrate the kernel’s effectiveness in visualizing and slicing models with complex defining functions and detailed geometry, and utilize the geometry kernel to manufacture a physical part. Additionally, we present performance metrics across multiple hardware configurations demonstrating significant performance improvements over existing F-Rep geometry kernels, and we examine how our geometry kernel scales with computing power.

Application of SLAM in endoscopic imaging

This paper presents the utilization of Simultaneous Localization and Mapping (SLAM) technology in medical endoscopic imaging. The fundamental components, hardware configuration, and process of SLAM are introduced in detail, with a focus on sensor acquisition, data preprocessing, feature extraction and matching, state estimation and update, map construction, and optimization modules. The application of SLAM in the medical field is then discussed, specifically highlighting real-time localization and reconstruction of endoscopic imaging. The integration of SLAM technology can assist doctors in accurately identifying the site of lesions, thereby enhancing surgical precision and safety. Furthermore, the paper introduces various commonly used SLAM algorithms, including the Kalman filter, extended Kalman filter, particle filter, optimization algorithms, among others. It emphasizes the significance of algorithm selection and optimization tailored to different scenarios and requirements. In conclusion, the application of SLAM technology in medical endoscopy imaging holds immense potential, offering improved accuracy and visualization during surgical procedures. This technology provides valuable support for doctors in diagnosis and treatment, ultimately enhancing patient outcomes.

Cuckoo Search Algorithm Study for Voltage Source Inverter Harmonic Reduction

This research examines the issues with harmonics in a voltage source inverter (VSI) and proposes the cuckoo search optimization (CSO) algorithm for reducing THD value. This algorithm has been employed to estimate the optimum values of the PI constants. Utilizing the traditional PI controller attached VSI, the tuned values of PI constants determined by the CSO algorithm are employed to reduce harmonics. The Simulink tool of the MATLAB program and the hardware configuration employing the PIC microcontroller were used to carry out the experimental verifications. The estimated THD values for both the software and the hardware results demonstrate that the THD values are significantly lower than the requirements set out by the IEEE standard.

Functional Meta-Analysis of the Proteomic Responses of Arabidopsis Seedlings to the Spaceflight Environment Reveals Multi-Dimensional Sources of Variability across Spaceflight Experiments.

The human quest for sustainable habitation of extraterrestrial environments necessitates a robust understanding of life's adaptability to the unique conditions of spaceflight. This study provides a comprehensive proteomic dissection of the Arabidopsis plant's responses to the spaceflight environment through a meta-analysis of proteomics data from four separate spaceflight experiments conducted on the International Space Station (ISS) in different hardware configurations. Raw proteomics LC/MS spectra were analyzed for differential expression in MaxQuant and Perseus software. The analysis of dissimilarities among the datasets reveals the multidimensional nature of plant proteomic responses to spaceflight, impacted by variables such as spaceflight hardware, seedling age, lighting conditions, and proteomic quantification techniques. By contrasting datasets that varied in light exposure, we elucidated proteins involved in photomorphogenesis and skotomorphogenesis in plant spaceflight responses. Additionally, with data from an onboard 1 g control experiment, we isolated proteins that specifically respond to the microgravity environment and those that respond to other spaceflight conditions. This study identified proteins and associated metabolic pathways that are consistently impacted across the datasets. Notably, these shared proteins were associated with critical metabolic functions, including carbon metabolism, glycolysis, gluconeogenesis, and amino acid biosynthesis, underscoring their potential significance in Arabidopsis' spaceflight adaptation mechanisms and informing strategies for successful space farming.

Operations, IT, and construction time orientations and the challenges of implementing IOT

The adoption of Internet of Things has grown significantly in recent years both to address sustainability in campus operations and as part of digital twin systems. This study looks at in-depth cases of large university campus owners and the challenges that this IOT introduces for the maintenance and management of these systems and the data they collect. In this ethnography there are three main time orientations related to facilities management (Facilities), Information Technology (IT), and Capital Projects. First, a university campus is like a small city, with buildings, utilities, and transportation systems - taken together we call this campus infrastructure (buildings 50-100, roads and utilities 20-50 years). Second, IT employees think on 2–3-month scale, working through implementing software and hardware upgrades, configurations and patches, at times needing agile operations to deal with emerging cybersecurity threats. Third, in Capital Projects the design phase can last 9 months, and the construction from 1 - 2 years for a typical project, and this is where IOT technologies are often first introduced into campus. While the capital project teams reflect on the user experience, these teams are often removed from the realities of facilities management and do not understand the time scales or the scope of the work that is required to manage a portfolio of Facilities and IT systems. In this paper, we explore how these time orientations lead to tensions in the owners’ selection of IOT devices and systems, in the integration of new technologies into existing systems, and in the operations of keeping existing systems up and running for the longer time scales of campus infrastructure life spans. Furthermore, this paper presents a paradox: If they speed up, they lose things, if they slow down, they lose other things, and presents ways that owner organizations manage this paradox through temporal boundary spanners who understand the disciplinary requirements, cultures, and frameworks across the organization and helps to mitigate the tensions across these differences.

GREEN ALGORITHM: MEASURING THE CARBON FOOTPRINT

The effects of climate change have a significant impact on nearly every aspect of life on Earth, including human civilizations, the economy, and health. Data centers and other large-scale computing resources are among the human activities responsible for large greenhouse gas (BSG) emissions. Other human activities also contribute to this problem. Although the growth of high-performance computing has allowed many important scientific breakthroughs to be achieved, the environmental impact of this progress cannot be underestimated. This paper describes a methodological framework for consistently and reliably estimating the carbon footprint of any computer business. The system can be used to calculate the carbon footprint of any computing task. A user can measure and report the carbon footprint of calculations using a newly created web tool called the Green algorithm (www.green-algorithms.org), which is freely available to the public. However, the tool is very simple to implement within computational procedures, as it uses very little information, does not interfere with pre-written code, and can accommodate a wide variety of hardware configurations. Taken as a whole, this research offers a simple, generalizable framework, as well as an accessible tool for measuring the carbon footprint of almost any computation.

Design and Research of a Field Bus Control System Laboratory for Metal Mining, Beneficiation and Metallurgy

Fieldbus control systems play a pivotal role in industries such as mining, beneficiation, and metallurgy, facilitating precise process control. However, diverse process conditions and applications often lead to challenges during system implementation. The prevalence of process control projects underscores the need for dedicated control system laboratories to address these problems. Our research delves into the complexities of process control systems, focusing on mainstream brands such as Siemens, Rockwell, and Emerson, involving analysis of network architectures, software, and hardware configurations. Through rigorous testing of real equipment systems, we uncover prevalent issues in practical control system applications. These findings guide the resolution of technical challenges faced in project control, concurrently enhancing the design and debugging prowess of engineering professionals. We also anticipate the trajectory of intelligent manufacturing, embracing collaborative manufacturing aspects in networked environments. This research establishs a robust foundation for the forthcoming generation of control network technologies specific to metal mining, beneficiation, and metallurgy.

Velocity range-based reward shaping technique for effective map-less navigation with LiDAR sensor and deep reinforcement learning.

In recent years, sensor components similar to human sensory functions have been rapidly developed in the hardware field, enabling the acquisition of information at a level beyond that of humans, and in the software field, artificial intelligence technology has been utilized to enable cognitive abilities and decision-making such as prediction, analysis, and judgment. These changes are being utilized in various industries and fields. In particular, new hardware and software technologies are being rapidly applied to robotics products, showing a level of performance and completeness that was previously unimaginable. In this paper, we researched the topic of establishing an optimal path plan for autonomous driving using LiDAR sensors and deep reinforcement learning in a workplace without map and grid coordinates for mobile robots, which are widely used in logistics and manufacturing sites. For this purpose, we reviewed the hardware configuration of mobile robots capable of autonomous driving, checked the characteristics of the main core sensors, and investigated the core technologies of autonomous driving. In addition, we reviewed the appropriate deep reinforcement learning algorithm to realize the autonomous driving of mobile robots, defined a deep neural network for autonomous driving data conversion, and defined a reward function for path planning. The contents investigated in this paper were built into a simulation environment to verify the autonomous path planning through experiment, and an additional reward technique "Velocity Range-based Evaluation Method" was proposed for further improvement of performance indicators required in the real field, and the effectiveness was verified. The simulation environment and detailed results of experiments are described in this paper, and it is expected as guidance and reference research for applying these technologies in the field.

Enhancing Electro-Hydraulic Load Simulator Performance Through Variable Arm Length and Particle Swarm-Optimized Controllers

This study presents a novel approach to improve the loading characteristics of an electro-hydraulic load simulator by introducing a variable arm length mechanism and employing advanced control strategies rooted in Particle Swarm Optimization (PSO). The design enhancements focus on optimizing the physical connection between the steering gear system and the load system, resulting in an electro-hydraulic load simulator equipped with adjustable arm length. Through a meticulous analysis of the ideal pivot position for the rocker lever, a fuzzy Proportional-Integral-Derivative (PID) controller based on PSO has been developed for the steering gear system. In recognition of the PSO algorithm’s susceptibility to local optima convergence, a fuzzy PID controller employing Single-Particle Random Search (SSPSO) has also been devised for the load system. To assess the effectiveness of this innovative hardware configuration and controller implementation in mitigating over-suppression, comprehensive joint simulation experiments were conducted. The results unequivocally indicate that the incorporation of the variable arm length structure, along with the PSO-based fuzzy PID controller optimized using binding factors, substantially enhances the dynamic loading performance of the electro-hydraulic load simulator. This enhancement translates into superior loading accuracy and overall performance metrics, establishing this research as a valuable reference for future investigations in this field.

A Quantitative Assessment of the Impact of Automated Incident Response on Cloud Services Availability

Abstract In the rapidly evolving landscape of cloud computing, ensuring high service availability is paramount for organizations reliant on digital infrastructure. This study conducts a quantitative assessment of the impact of Automated Incident Response Systems (AIRS) on the availability of cloud services. Utilizing a dataset derived from multiple cloud service providers over a twelve-month period, we analyze key performance metrics, including incident response times, resolution rates, and service uptime statistics, both pre- and post-AIRS implementation. The results indicate a statistically significant improvement in service availability following the deployment of AIRS, characterized by reduced incident resolution times and enhanced operational uptime. Furthermore, we present a comparative analysis of traditional incident response methodologies versus automated systems, demonstrating the superior efficiency and reliability of AIRS. The findings underscore the necessity for organizations to embrace automation in incident management to bolster service resilience and optimize customer satisfaction. This study concludes with practical recommendations for organizations considering the integration of AIRS into their operational frameworks. In the rapidly evolving landscape of cloud computing, ensuring high service availability has become a critical concern for organizations that depend heavily on digital infrastructures. This study conducts a comprehensive quantitative assessment of the impact of Automated Incident Response Systems (AIRS) on the availability of cloud services. As cloud environments become increasingly complex, the potential for service disruptions due to incidents—such as hardware failures, security breaches, or configuration errors—poses significant risks to businesses. Through a detailed analysis of incident response metrics obtained from a diverse dataset comprising five leading cloud service providers, this research evaluates key performance indicators including incident response times, resolution rates, and service uptime statistics over a twelve-month period. Employing rigorous statistical methods, the study investigates the relationship between the implementation of AIRS and enhancements in service availability, offering insights into the effectiveness of automation in incident management. The results reveal a statistically significant improvement in service availability post-AIRS deployment, characterized by a dramatic reduction in average incident response times—from 30 minutes to just 10 minutes—and an increase in resolution rates from 75% to 95%. Furthermore, the average service uptime improved from 90% to an impressive 99.5%, demonstrating the potential for organizations to achieve greater operational resilience through the integration of automated systems. The findings also include a comparative analysis of traditional versus automated incident response methodologies, highlighting the superior efficiency and reliability of AIRS. This study emphasizes the necessity for organizations to adopt automated solutions in their incident management frameworks to mitigate the risks associated with downtime and enhance customer satisfaction. The conclusions drawn from this research provide practical recommendations for businesses considering AIRS integration, making a compelling case for the strategic implementation of automated incident response mechanisms in the pursuit of optimal service availability.

Fault-Tolerant Hardware Acceleration for High-Performance Edge-Computing Nodes

High-performance embedded systems with powerful processors, specialized hardware accelerators, and advanced software techniques are all key technologies driving the growth of the IoT. By combining hardware and software techniques, it is possible to increase the overall reliability and safety of these systems by designing embedded architectures that can continue to function correctly in the event of a failure or malfunction. In this work, we fully investigate the integration of a configurable hardware vector acceleration unit in the fault-tolerant RISC-V Klessydra-fT03 soft core, introducing two different redundant vector co-processors coupled with the Interleaved-Multi-Threading paradigm on which the microprocessor is based. We then illustrate the pros and cons of both approaches, comparing their impacts on performance and hardware utilization with their vulnerability, presenting a quantitative large-fault-injection simulation analysis on typical vector computing benchmarks, and comparing and classifying the obtained results. The results demonstrate, under specific conditions, that it is possible to add a hardware co-processor to a fault-tolerant microprocessor, improving performance without degrading safety and reliability.

Adaptive Image Size Padding for Load Balancing in System-on-Chip Memory Hierarchy

The conventional address map often incurs traffic congestion in on-chip memory components and degrades memory utilization when the access pattern of an application is not matched with the address map. To reduce traffic congestion and improve the memory system performance, we propose an adaptive image size padding technique for a given address mapping and a hardware configuration. In the presented software approach, the system can adaptively determine the image pad size at the application-invoke time to enhance the load balancing across the on-chip memory hierarchy. Mainly targeting a high-bandwidth image processing application running in a device accelerator of an embedded system, we present the design, describe the algorithm, and conduct the performance experiment. As a result, the experiments indicate the presented design can improve load balancing up to 95% and performance up to 35%, with insignificant memory footprint overheads.