Computer System Architecture Research Articles

An Evaluation of the Security of Bare Machine Computing (BMC) Systems against Cybersecurity Attacks

The Internet has become the primary vehicle for doing almost everything online, and smartphones are needed for almost everyone to live their daily lives. As a result, cybersecurity is a top priority in today’s world. As Internet usage has grown exponentially with billions of users and the proliferation of Internet of Things (IoT) devices, cybersecurity has become a cat-and-mouse game between attackers and defenders. Cyberattacks on systems are commonplace, and defense mechanisms are continually updated to prevent them. Based on a literature review of cybersecurity vulnerabilities, attacks, and preventive measures, we find that cybersecurity problems are rooted in computer system architectures, operating systems, network protocols, design options, heterogeneity, complexity, evolution, open systems, open-source software vulnerabilities, user convenience, ease of Internet access, global users, advertisements, business needs, and the global market. We investigate common cybersecurity vulnerabilities and find that the bare machine computing (BMC) paradigm is a possible solution to address and eliminate their root causes at many levels. We study 22 common cyberattacks, identify their root causes, and investigate preventive mechanisms currently used to address them. We compare conventional and bare machine characteristics and evaluate the BMC paradigm and its applications with respect to these attacks. Our study finds that BMC applications are resilient to most cyberattacks, except for a few physical attacks. We also find that BMC applications have inherent security at all computer and information system levels. Further research is needed to validate the security strengths of BMC systems and applications.

Estimating Power, Performance, and Area for On-Sensor Deployment of AR/VR Workloads Using an Analytical Framework

Augmented Reality and Virtual Reality have emerged as the next frontier of intelligent image sensors and computer systems. In these systems, 3D die stacking stands out as a compelling solution, enabling in situ processing capability of the sensory data for tasks such as image classification and object detection at low power, low latency, and a small form factor. These intelligent 3D CMOS Image Sensor (CIS) systems present a wide design space, encompassing multiple domains (e.g., computer vision algorithms, circuit design, system architecture, and semiconductor technology, including 3D stacking) that have not been explored in-depth so far. This article aims to fill this gap. We first present an analytical evaluation framework, STAR-3DSim, dedicated to rapid pre-RTL evaluation of 3D-CIS systems capturing the entire stack from the pixel layer to the on-sensor processor layer. With STAR-3DSim, we then propose several knobs for PPA (power, performance, area) improvement of the Deep Neural Network (DNN) accelerator that can provide up to 53%, 41%, and 63% reduction in energy, latency, and area, respectively, across a broad set of relevant AR/VR workloads. Last, we present full-system evaluation results by taking image sensing, cross-tier data transfer, and off-sensor communication into consideration.

State Machines for Large Scale Computer Software and Systems

The behavior and architecture of large scale discrete state systems found in computer software and hardware can be specified and analyzed using a particular class of primitive recursive functions. This article begins with an illustration of the utility of the method via a number of small examples and then via longer specification and verification of the “Paxos” distributed consensus algorithm [ 26 ]. The “sequence maps” are then shown to provide an alternative representation of deterministic state machines and products of state machines. Distributed and composite systems, parallel and concurrent computation, and real-time behavior can all be specified naturally with these methods—which require neither extensions to the classical state machine model nor any axiomatic methods or other techniques from formal logic or other foundational methods. Compared to state diagrams or tables or the standard set-tuple-transition-maps, sequence maps are more concise and better suited to describing the behavior and compositional architecture of computer systems. Staying strictly within the boundaries of classical deterministic state machines anchors the methods to the algebraic structures of automata and makes the specifications faithful to engineering practice.

Research on optical computing system architecture for simple recurrent neural networks

Moore’s Law, relating to the speed and capabilities of computers is becoming less applicable. In this ‘post-Moore’ era, a cross-disciplinary team based in the Constructive Electronics Laboratory, Kyushu University, Japan, is investigating optical computing system infrastructures, with a view to driving computing technology forward in a way that negates the need to comply with Moore’s Law. Associate Professor Satoshi Kawakami is an expert in electric circuits and computer architecture who is part of the team. The team’s expertise covers materials, devices, circuits, architectures and algorithms and is geared towards pioneering new computing technologies in the post-Moore era. Kawakami believes that the continuous improvement of computer systems with higher performance and lower power consumption/energy consumption will be essential to realise a sustainable advanced information society and wants to maximise the advantages of devices and hide their disadvantages at the system level, which will necessitate collaboration with higher system layers. Another important goal is reducing power consumption by improving the efficiency of computers. In one current project, the researchers are exploring optical computing system infrastructure for simple recurrent neural networks. The team is keen to re-examine the ideal state of optical circuits from the perspective of the entire system, including electrical memory and interfaces.

Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co-Fe-B)x(LiNbO3)100-x nanocomposite memristors.

MLP-Mixer based on multilayer perceptrons (MLPs) is a novel architecture of a neuromorphic computing system (NCS) introduced for image classification tasks without convolutional layers. Its software realization demonstrates high classification accuracy, although the number of trainable weights is relatively low. One more promising way of improving the NCS performance, especially in terms of power consumption, is its hardware realization using memristors. Therefore, in this work, we proposed an NCS with an adapted MLP-Mixer architecture and memristive weights. For this purpose, we used a passive crossbar array of (Co-Fe-B)x(LiNbO3)100-x memristors. Firstly, we studied the characteristics of such memristors, including their minimal resistive switching time, which was extrapolated to be in the picosecond range. Secondly, we created a fully hardware NCS with memristive weights that are capable of classification of simple 4-bit vectors. The system was shown to be robust to noise introduction in the input patterns. Finally, we used experimental memristive characteristics to simulate an adapted MLP-Mixer architecture that demonstrated a classification accuracy of (94.7 ± 0.3)% on the Modified National Institute of Standards and Technology (MNIST) dataset. The obtained results are the first steps toward the realization of memristive NCS with a promising MLP-Mixer architecture.

Situation identification in smart wearable computing systems based on machine learning and Context Space Theory

Wearable devices and smart sensors are increasingly adopted to monitor the behaviors of human and artificial agents. Many applications rely on the capability of such devices to recognize daily life activities performed by the monitored users in order to tailor their behaviors with respect to the occurring situations. Despite the constant evolution of smart sensing technologies and the numerous research in this field, an accurate recognition of in-the-wild situations still represents an open research challenge. This work proposes a novel approach for situation identification capable of recognizing the activities and the situations in which they occur in different environments and behavioral contexts, processing data acquired by wearable and environmental sensors.An architecture of a situation-aware wearable computing system is proposed, inspired by Endsley’s situation-awareness model, consisting of a two-step approach for situation identification. The approach first identifies the daily life activities via a learning-based technique. Simultaneously, the context in which the activities are performed is recognized using Context Space Theory. Finally, the fusion between the context state and the activities allows identifying the complex situations in which the user is acting. The knowledge regarding the situations forms the basis on which novel and smarter applications can be realized.The approach has been evaluated on the ExtraSensory public dataset and compared with state-of-the-art techniques, achieving an accuracy of 96% for the recognition of situations and with significantly low computational time, demonstrating the efficacy of the two-step situation identification approach.

Unraveling quantum computing system architectures: An extensive survey of cutting-edge paradigms

Context:The convergence of physics and computer science in the realm of quantum computing systems has sparked a profound revolution within the computer industry. However, despite such promise, the existing focus on quantum software systems primarily centers on the generation of quantum source code, inadvertently overlooking the pivotal role of the overall software architecture. Objectives:In order to provide comprehensive guidance to researchers and practitioners engaged in quantum software development, employing an architecture-centered development model, an extensive literature review was conducted pertaining to existing research on quantum software architecture. The analysis encompasses a detailed examination of the characteristics exhibited by these studies and the identification of prospective challenges that lie ahead in the field of quantum software architecture. Methods:We have closely examined instances of quantum software engineering, quantum modeling languages, quantum design patterns, and quantum communication security to gain insights into the distinctive attributes associated with various software architecture approaches. Results:Our findings underscore the critical significance of prioritizing software architecture in the development of robust and efficient quantum software systems. Through the synthesis of these multifaceted aspects, both researchers and practitioners can devise quantum software solutions that are inherently architecture-centric. Conclusion:The software architecture of quantum computing systems plays a pivotal role in determining their ultimate success and usability. Given the ongoing advancements in quantum computing technology, the migration of traditional software architecture development methods to the domain of quantum software development holds significant importance.

Ruang Lingkup Organisasi Dan Arsitektur Komputer

In computer system architecture, the role of the processor cannot be separated as the main component in the operation of the computer operating system. CPU (Central Processing Unit), or commonly called a processor, is a part in the form of a rectangular chip or integrated circuit, the main component of a computer system, which functions to control computer operations, supported by other departments. The unit of processing speed is Mega Heartz or 1000 MegaHeartz. The higher the value, the faster the process runs on the computer. Processor development has progressed so rapidly from year to year that even pioneers such as Intel and AMD are still competing to this day.

Investigation of reconfigurable hardware platformsfor 5Gprotocol stack functions acceleration

Open RAN and 5G are two key technologies designed to qualitatively improve network infrastructure and provide greater flex-ibility and efficiency to mobile operators and users. 5G creates new capabilities for high-speed Internet, Internet of Things, telemedi-cine and many other applications, while Open RAN enables open and standardized network architectures, which reduces cost and risk for operators and promotes innovations. Given the growing number of users and data volumes, the purely software implementa-tion of certain functions of the 5G protocol, and especially computationally complex ones, requires significant computer resources and energy.These, for example, arelow-density parity-check (LDPC)coding,FFTandiFFT algorithms on physical (PHY)layer, and NEA and NIA security algorithms on Packet Data Convergence Protocol (PDCP)layer. Therefore, one of the activity areas in the development of means for 5G systems isthe hardware acceleration of such functions execution, which provides the possibility of processing large volumes of data in real time and with high efficiency. The high-performance hardware basis for implementing these functions today is field-programmable gate array(FPGA)integrated circuits.Along with this, the efficiency of the 5G protocol stack functions hardware acceleration depends significantly on the size of the data packets transmitted to the hardware accelerator. As ex-perience shows, for certain types of architecture of computer systems with accelerators, the acceleration value can take even a nega-tive value. This necessitates the search for alternative architectural solutions for the implementation of such systems.In this article the approaches for hardware acceleration using reconfigurable FPGA-based computing components are explored, their comparative analysis is performed, and architectural alternatives are evaluated for the implementation of a computing platform to perform the functions ofthe 5G protocol stack with hardware acceleration of PHY and medium access control(MAC)layers functions.

MSI-A: An Energy Efficient Approximated Cache Coherence Protocol

Energy consumption has become an essential factor in designing modern computer system architecture. Because of physical limits, the termination of Moore’s law and Dennard’s scaling has forced the computer design community to investigate new approaches to meet the requirements for computing resources. Approximate computing has emerged as a promising method for reducing energy consumption while trading a controllable quality loss. This paper asserts that an approximated cache coherence protocol preserves overall energy for computation. We can approximate the cache coherence protocol by adding approximated cache lines to a certain level without hindering the output. This paper introduces an enhanced approximated version of the MSI (Modified Shared Invalid) cache coherence protocol MSI-A (Modified Shared Invalid-Approx). We have verified MSI-A and MSI by employing LTL specifications in the NuSMV model checker. To illustrate the benefits of MSI-A, we have added DTMC (Discrete-Time Markov Chain) with PCTL (Probabilistic Computational Tree Logic). Although the PCTL proves the theory of approximation, we have also simulated the MSI-A in the TEJAS hardware simulator on PARSEC 3.0 to investigate the energy gains and cycle gains of MSI-A in varied applications. The cache lines considered to be approx are between 10 and 30 percent. Each application benefited from approximation according to its nature, and VIPS has indicated a total energy gain of 30.18 percent.

Multithreaded Convolution Implementation Based on Block Methods

A multithreaded convolution implementation based on block algorithms is considered. Convolution is essentially the basis of many methods that solve the problem of determining the degree of similarity or independence of two processes, in other words, when it is necessary to determine the degree of correlation. The algorithm itself is executed with a significant delay, because for its execution it is necessary to accumulate the entire signal and then process it. The analysis showed that one of the possible ways to reduce time costs is a multithreaded implementation of convolution based on block algorithms. The article shows the main features of the convolution implementation by the overlap method with addition and the overlap method with addition, as well as numerical examples. The results obtained show that the application of these methods without the use of a window function leads to significant distortions in the signal spectrum. Based on the results of the analysis, a universal scheme for performing convolution based on multithreaded processing of an input data block is proposed. This allows to achieve a good compromise between computational complexity, system architecture, and time costs.

A Study of Intelligent Paper Grouping Model for Adult Higher Education Based on Random Matrix

This paper presents a comprehensive study and analysis of the intelligent grouping of papers in adult higher education using a random matrix approach. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct a subspace weighting matrix. The statistical properties of the sample covariance matrix eigenvectors are analyzed using the first-order perturbation approximation, and then, asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspace to the real signal parametrization are used to obtain the weighting matrix based on the random matrix eigenvectors. Dynamic adjustment according to the fitness of individuals in the population is performed to ensure population diversity, while the combination of the small habitat technique can avoid the algorithm from falling into early convergence. The algorithm introduces chaos theory to optimize the population initialization process and uses the dynamic traversal randomness of chaos to select individuals in the population so that the initial population is close to the desired target solution. The design of the fitness function in the genetic algorithm generally maps the objective function of the problem to the fitness function. A good fitness function can directly reflect the quality of the individuals in the group. Based on the in-depth study of the basic attributes of the test questions and the principles of test paper evaluation, the mathematical model and objective function of intelligent paper grouping are determined by the difficulty, knowledge points, and cognitive level of the test questions as the main constraints, and NCAGA is applied to the intelligent paper grouping method, which better completes the intelligent paper grouping session for the computer system architecture course. In the process of designing the intelligent grouping algorithm, for the situations of premature convergence and convergence to locally optimal solutions that easily occur in the traditional genetic algorithm, this paper adopts the approach of adaptive adjustment of crossover probability and variation probability to improve the algorithm and achieves satisfactory results. Based on extensive business research, this paper completes the requirement analysis of the online practice system based on the intelligent grouping of papers and presents the functional design and database design of the key functional modules in the system in detail. Finally, this paper conducts functional tests on the system and analyses the test results.

Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

This article presents a crowd monitoring system based on the passive detection of probe requests. The system meets strict privacy requirements and is suited to monitoring events or buildings with a least a few hundred attendees. We present our counting process and an associated mathematical model. From this model, we derive a concentration inequality that highlights the accuracy of our crowd count estimator. Then, we describe our system. We present and discuss our sensor hardware, our computing system architecture, and an efficient implementation of our counting algorithm—as well as its space and time complexity. We also show how our system ensures the privacy of people in the monitored area. Finally, we validate our system using nine weeks of data from a public library endowed with a camera-based counting system, which generates counts against which we compare those of our counting system. This comparison empirically quantifies the accuracy of our counting system, thereby showing it to be suitable for monitoring public areas. Similarly, the concentration inequality provides a theoretical validation of the system.

Analysis and Research on the Characteristics of Modern English Classroom Learners’ Concentration Based on Deep Learning

There are some problems in modern English education, such as difficulties in classroom teaching quality evaluation, lack of objective evaluation basis in teaching process management, and quality monitoring. The development of artificial intelligence technology provides a new idea for classroom teaching evaluation, but the existing classroom evaluation scheme based on artificial intelligence technology has a series of problems such as high system cost, low evaluation accuracy, and incomplete evaluation. In view of the above problems, this paper proposes a solution of English classroom concentration evaluation system based on deep learning. The program studies the evaluation methods of students’ class concentration, class activity, and enrichment degree of teaching links, and constructs an information evaluation system of students’ learning process and class teaching quality. Based on the edge computing system architecture, a hardware platform with cloud platform AI+ embedded visual edge computing devices managed by an FPGA deep learning accelerated server was built. The design, debugging, and testing of classroom evaluation and student behavior statistics-related functions were completed. This scheme uses edge computing hardware architecture to solve the problem of high system cost. Deep learning technology is used to solve the problem of low accuracy of classroom evaluation. It mainly evaluates the classroom objectively by extracting indicators such as the students' attention in the classroom, and solves the problems of the students’ inattentiveness in the classroom. After the test, the classroom evaluation system designed by the paper runs stably and all functions run normally. The test results show that the system can basically meet the requirements of classroom teaching evaluation application.

Signal performance optimization in the local area network trafic management in the DRC : Models for transmission networks

The high availability of computer networks is a prerequisite in large companies and service providers. Thus, computer network administrators are called upon to face the various growing challenges related to unscheduled downtime of services; lack of expertise; lack of tools; the complexity of technologies; market consolidation and competition to provide better quality services. It should be remembered that the organization of the networks of telecommunication aims at giving control of the phenomena which occur there, during the treatment of the communications, and more generally of the services which they offer. These phenomena are governed by the randomness of the appearance of the requests, and are studied independently of the choices of technology implemented. They are amenable to the formalism of the calculation of probabilities, and give birth to the notion of traffic, which will play a central role in their apprehension, given that they condition for a large part the effective structure of modern networks. This research specifically examines the problems of optimization of signal performance in traffic management of local area networks of companies in the Democratic Republic of Congo, by presenting the different elements of network traffic theory and quality. Thus demonstrating their impact on the architectures of networks and computer systems, and at the same time introducing the major problems faced by IT network administrators on a daily basis; The main objective of this research is to evaluate the development performance of computer and telecommunications networks according to their applications in the areas of network design, equipment dimensioning, and characterization and quality measurement. Service, and network planning techniques.

Exploring the Galaxyfly Family to Build Flexible-Scale Interconnection Networks

Interconnection networks play an essential role in the architecture of high-performance computing (HPC) systems. In this article, we explore the Galaxyfly family to build flexible-scale interconnection networks. Galaxyfly is guaranteed to retain a small constant diameter while achieving a flexible tradeoff between network scale and bisection bandwidth. Galaxyfly not only supports small-scale interconnection networks with smaller diameter but also lowers the demands for high-radix routers and is able to utilize routers with moderate radix to build exascale interconnection networks. We analyze the constructible configuration of Galaxyfly and evaluate the properties of Galaxyfly. We conduct extensive simulations and analysis to evaluate the performance, cost, and power consumption of Galaxyfly on physical layout against state-of-the-art topologies. The results show that our design achieves better performance than most existing topologies under typical HPC workloads, and is cost-effective to deploy for exascale HPC systems.

Development Model of Enterprise Green Marketing Based on Cloud Computing

Internet-based cloud computing is currently an important core technology for computer development in China. It can be used not only in marketing but also in various industries. At the same time, people-oriented and green projects and products that focus on the development of the ecological environment and green consumption dominate the current market trend. The purpose of this paper is at studying the development model of enterprise green marketing based on cloud computing. This article compares and analyzes enterprise green marketing systems through big data algorithms and statistical methods. It starts with the basic characteristics of cloud computing, studies the opportunities and challenges that cloud computing brings to enterprises’ green marketing efforts, describes the green marketing processes and characteristics of enterprises, and analyzes cloud. The feasibility of the construction of the computing system, the basic architecture of the cloud computing system, and the construction of a complete cloud computing data processing flow are proposed. The research data found that the combination of the green marketing development model and cloud computing in the enterprise operating system is conducive to the development of the enterprise; it improves the discovery efficiency of the enterprise and reduces the pollution in the production of the enterprise; the cloud computing can greatly improve the work efficiency of the employees. Cloud computing can improve employees’ speed to complete tasks by about 20% and reduce the error rate by about 50%. The cloud computing enterprise green marketing development model has guiding significance for the long-term development of the enterprise.

Implementation of C5.0 Algorithm for Prediction of Student Learning Graduation in Computer System Architecture Subjects

Computer system architecture is one of the subjects that must be taken in the informatics engineering study program. In the study program the graduation of each student in the course is one of the important aspects that must be evaluated every semester. Graduation for each student / I in the course is an illustration that the learning process delivered is going well and also the material presented by the lecturer in charge of the course can be digested by students. Graduation of each student in the course can be predicted based on the habit pattern of the students. Data mining is an alternative process that can be done to find out habit patterns based on the data that has been collected. Data mining itself is an extraction process on a collection of data that produces valuable information for companies, agencies or organizations that can be used in the decision-making process. Prediction of graduation with data mining can be solved by classifying the data set. The C5.0 algorithm is an improvement algorithm from the C4.5 algorithm where the process is almost the same, only the C5.0 algorithm has advantages over the previous algorithm. The results of the C5.0 algorithm are in the form of a decision tree or a rule that is formed based on the entropy or gain value. The prediction process is carried out based on the C5.0 algorithm classification using the attributes of Attendance Value, Assignment Value, UTS Value and UAS Value. The final result of the C5.0 algorithm classification process is a decision tree with rules in it.

Architecture of Computing System based on Chiplet.

Computing systems are widely used in medical diagnosis, climate prediction, autonomous vehicles, etc. As the key part of electronics, the performance of computing systems is crucial in the intellectualization of the equipment. The conflict between performance, efficiency, and cost can be solved by choosing an appropriate computing system architecture. In order to provide useful advice and instructions for the designers to fabricate high-performance computing systems, this paper reviews the Chiplet-based computing system architectures, including computing architecture and memory architecture. Firstly, the computing architecture used for high-performance computing, mobile, and PC is presented and summarized. Secondly, the memory architecture based on mainstream memory and emerging non-volatile memory used for data storing and processing are introduced, and the key parameters of memory are compared and discussed. Finally, this paper is concluded, and the future perspectives of computing system architecture based on Chiplet are presented.

О ПРИМЕНЕНИИ ПРАВООХРАНИТЕЛЬНЫМИ ОРГАНАМИ СПЕЦИАЛИЗИРОВАННОГО ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ В ОРГАНИЗАЦИИ ИСПОЛЬЗОВАНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

The article reveals the issue of improving the operational and investigative support for countering crime by law enforcement agencies through the use of specialized software in the organization of the use of artificial intelligence. The development of information technologies requires the improvement of methods and means of law enforcement activities. Modern realities have formed the need to use the capabilities of artificial intelligence to solve law enforcement tasks. Due to the increase in the computing capabilities of software and hardware complexes, including as a result of the use of graphics processors and distributed computing system architectures, a wide application of machine learning based on a variety of computing systems organized on the principle of neural networks has become available. As an effective tool for countering crime, law enforcement agencies need to have in their arsenal tools for not only monitoring the information space and getting real-time access to information that is stored on a user’s smartphone or computer, on cloud storage, in social networks, on built-in or portable storage devices. Get access to information posted, sent, stored on remote servers, devices of the person being developed through remote monitoring, be able to process large amounts of data and solve complex problems faster than the traditional algorithms currently existing. To obtain the necessary data, it is necessary to use specialized information software of the network environment aimed at solving the tasks of solving and investigating crimes.