Central Processing Unit Research Articles

Lightweight Hardware-Based Cache Side-Channel Attack Detection for Edge Devices (Edge-CaSCADe)

Cache Side Channel Attacks (CSCA) have been haunting most processor architectures for decades now. Existing approaches to mitigation of such attacks have certain drawbacks namely software mishandling, performance overhead, low throughput due to false alarms, etc. Hence, “mitigation only when detected” should be the approach to minimize the effects of such drawbacks. We propose a novel methodology of fine-grained detection of timing-based CSCA using a hardware-based detection module. We discuss the design, implementation, and use of our proposed detection module in processor architectures. Our approach successfully detects attacks that flush secret victim information from cache memory like Flush+Reload, Flush+Flush, Prime+Probe, Evict+Probe, and Prime+Abort, commonly known as cache timing attacks. Detection is on time with minimal performance overhead. The parameterizable number of counters used in our module allows detection of multiple attacks on multiple sensitive locations simultaneously. The fine-grained nature ensures negligible false alarms, severely reducing the need for any unnecessary mitigation. The proposed work is evaluated by synthesizing the entire detection algorithm as an attack detection block, Edge-CaSCADe, in a RISC-V processor as a target example. The detection results are checked under different workload conditions with respect to the number of attackers, the number of victims having RSA,AES and ECC based encryption schemes like ECIES, and on benchmark applications like MiBench and Embench. More than \(98\% \) detection accuracy within \(2\% \) of the beginning of an attack can be achieved with negligible false alarms. The detection module has an area and power overhead of \(0.9\% \) to \(2\% \) and \(1\% \) to \(2.1\% \) for the targeted RISC-V processor core without cache for 1 to 5 counters, respectively. The detection module does not affect the processor critical path and hence has no impact on its maximum operating frequency.

Development and Implementation of Visualization of CPU Components using AR

Augmented Reality (AR) technology presents innovative solutions for visualizing complex concepts and systems. This paper investigates the utilization of AR in the visualization of CPU (Central Processing Unit) components, with the objective of augmenting comprehension and learning within computer architecture. By developing an AR application, CPU components are depicted in three dimensions, enabling users to interactively explore and comprehend their functionalities and interconnections. The paper delineates the design and execution of the AR system and discusses its potential advantages for education, training, and technical problem-solving in computer science and engineering. Keywords: Augmented Reality, CPU Components, Visualization, Computer Architecture.

Training mode of university dance performers based on sequential quadratic programming algorithm

Dance performance is an art form, which needs to cultivate students’ dance skills, artistic accomplishment and stage performance ability. Sequential quadratic programming algorithm is an optimization algorithm that can be used to solve complex optimization problems. In this paper, Sequential Quadratic Programming (SQP) is applied to explore the training mode of dance performers in colleges to help dance performers develop the optimal training plan and program. Aiming at the problems existing in the training mode of dance performance talents in colleges, this paper put forward an optimization method based on SQP algorithm, and implemented its optimization scheme in actual colleges. In the planning of dance performance talent training mode, particle swarm optimization (PSO) is used to optimize SQP algorithm, so that it can have higher planning efficiency. This paper studied the goal and index system of the training of dance performance talents in colleges, generated a personalized training program, and further improved the scientific and practical effectiveness of the training mode. This paper investigated the current situation of dance performance talent training in several dance schools in a certain province of China. The survey data include practical curriculum planning, teachers’ teaching philosophy and teaching content. Combined with SQP algorithm, the teaching and training program is optimized. After evaluation, it can be concluded that the SQP algorithm optimized by PSO shows good stability and accuracy. It can calculate the optimal solution of the cultivation scheme, and when calculating the optimal solution, the running time of the Central Processing Unit (CPU) was only 5.6 s, which can further improve the efficiency of the planning. Finally, through the satisfaction and resource utilization test, it can be found that the number of people who are very satisfied with the teaching content of the dance performance talent training program optimized by SQP increased from 38.4% to 52.4%. After optimization, the average utilization rate of teaching resources reached 88.1%. It can be concluded that SQP algorithm can provide scientific basis for dance education institutions to improve the training mode of dance talents. This can help dance education institutions better improve the training mode, and improve the overall quality and dance skills of dance students.

Adaptive Methods for Revenue Model Learning of a Slice Broker in the Presence of Adversaries

AbstractIn the fifth-generation (5G) of mobile networks, Multi-Access Edge Computing (MEC) refers to the deployment of computing resources closer to the end-users for improved service delivery. In the context of 5G MEC, the slice broker plays a crucial role in managing the allocation of resources among the different network slices, which are logical networks on top of a shared infrastructure. The slice broker is a business entity that acts as an intermediary between the slice tenants and the infrastructure provider and is responsible for allocating resources (such as CPU, memory, and network bandwidth) required to set up the network. The slice broker must ensure that resources are allocated in a way that the revenue is maximized. In a dynamic environment, the slice broker must learn the revenue model adaptively and online. Adversaries can significantly reduce the revenue by misleading the system about the resources pretending to be selfish nodes, or creating noise. The slice broker should learn the revenue model in the presence of adversaries. We apply cooperative deep reinforcement learning with consensus mechanism and consensus deep learning to learn the revenue model adaptively. We also compare our proposed methods with the reference solution. Simulation results show that our proposed methods, especially the cooperative version, outperform the reference solution.

GSM BASED SMART SWITCH

This review paper delves into the design and im- plementation of GSM-based smart switches, which offer users the ability to remotely control electrical appliances using GSM networks. These devices incorporate a SIM card to facilitate com- munication through SMS commands and phone calls, enabling remote appliance control with ease. The fundamental components of this system include a microcontroller, a GSM module, a relay, and a dedicated power supply. The microcontroller, programmed in the preferred language, plays a crucial role in command processing and relay management. It serves as the system’s central processing unit, orchestrating relay operations to facilitate appliance on/off control. This approach provides users with a cost-effective, efficient, and accessible solution for home automa- tion and appliance management. The practical applications of this technology extend beyond convenience to include enhanced security and energy efficiency. Users can remotely activate or deactivate appliances, including lighting, security gates, and doors. This review paper not only fosters a deeper understanding of electronics and GSM networks but also highlights their potential to contribute to an improved quality of life, making it a subject of great interest within the realm of IoT, home automation, and agriculture firm. Index Terms—Keywords: GSM, SMS Commands, Motor, Smart Switch, Microcontroller.

Optimizing Resource Allocation in Cloud Computing Using Machine Learning

Purpose: A key component of large-scale distributed computing is the allocation of resources, as computer networks cooperate to address complex optimization problems. To get the most out of computers in general, or throughput, is the goal of resource allocation in this case. When it comes to distributed computing, there are two main varieties: grid computing and cloud computing. In grid computing, many geographically dispersed clusters are linked and made available to the general public. Materials and Methods: We looked at Monte Carlo Tree Search and Long-Short Term Memory and examined how efficient they were. By maintaining consistent traffic patterns, the simulation demonstrated that MCTS performance was improved. However, such a strategy is difficult to implement due to the potential speed with which traffic patterns may alter. A successful service level agreement (SLA) was achieved, and the issue was shown to be fixable using LSTM. We compare the suggested model to different load-balancing algorithms to find the one that best allocates resources. Findings: The results show that compared to the state-of-the-art models, the suggested model achieves an accuracy rate that is 10-15% higher. The suggested model lowers the error percentage rate of the average request blocking likelihood of traffic load by around 9.5-10.2% when compared to the predictions of existing models. Therefore, the proposed method has the potential to enhance network utilization by reducing the amount of time required by memory and the central processing unit. Implications to Theory, Practice and Policy: One advantage of the new method is a more robust forecasting strategy in comparison to earlier models. Using firefly algorithms, future research will construct a cloud data center that employs a variety of heuristics and machine learning methodologies to load balance the energy cloud (Oshawa et al., 2022).

PROTAX-GPU: a scalable probabilistic taxonomic classification system for DNA barcodes.

DNA-based identification is vital for classifying biological specimens, yet methods to quantify the uncertainty of sequence-based taxonomic assignments are scarce. Challenges arise from noisy reference databases, including mislabelled entries and missing taxa. PROTAX addresses these issues with a probabilistic approach to taxonomic classification, advancing on methods that rely solely on sequence similarity. It provides calibrated probabilistic assignments to a partially populated taxonomic hierarchy, accounting for taxa that lack references and incorrect taxonomic annotation. While effective on smaller scales, global application of PROTAX necessitates substantially larger reference libraries, a goal previously hindered by computational barriers. We introduce PROTAX-GPU, a scalable algorithm capable of leveraging the global Barcode of Life Data System (>14 million specimens) as a reference database. Using graphics processing units (GPU) to accelerate similarity and nearest-neighbour operations and the JAX library for Python integration, we achieve over a 1000 × speedup compared with the central processing unit (CPU)-based implementation without compromising PROTAX's key benefits. PROTAX-GPU marks a significant stride towards real-time DNA barcoding, enabling quicker and more efficient species identification in environmental assessments. This capability opens up new avenues for real-time monitoring and analysis of biodiversity, advancing our ability to understand and respond to ecological dynamics. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields.

Development of indicators to measure health system capacity for quality abortion care in 10 countries: a rapid assessment of a measurement framework and indicators

IntroductionA significant gap exists in the availability of indicators and tools to monitor health system capacity for quality abortion care at input and process levels. In this paper, we describe...

The updates in Libcint 6: More integrals, API refinements, and SIMD optimization techniques

Libcint is a library designed for the evaluation of analytical integrals for Gaussian type orbitals. It prioritizes simplicity, ease of use, and efficiency for the development of quantum chemistry programs. In the release of version 6.0, Libcint supports the computation of integrals for various operators, such as overlap, Coulomb, Gaunt, Breit, attenuated Coulomb, Slater-type geminals, and Yukawa potential, as well as arbitrary orders of derivatives for these operators. To enhance the usability of the library, Libcint provides a uniform function signature for all integral functions. A code generator is included to automate the implementation of new integrals. To achieve better performance on modern central processing unit architectures, the library employs explicit single instruction multiple data parallelization in the code implementation.

Survey on Redundancy Based-Fault tolerance methods for Processors and Hardware accelerators - Trends in Quantum Computing, Heterogeneous Systems and Reliability

Rapid progress in the CMOS technology for the past 25 years has increased the vulnerability of processors towards faults. Subsequently, focus of computer architects shifted towards designing fault-tolerance methods for processor architectures. Concurrently, chip designers encountered high order challenges for designing fault tolerant processor architectures. For processor cores, redundancy-based fault tolerance methods for fault detection at core level, micro-architectural level ,thread level , and software level are discussed. Similar applicable redundancy-based fault tolerance methods for cache memory, and hardware accelerators are presented in the article. Recent trends in fault tolerant quantum computing and quantum error correction are also discussed. The classification of state-of-the-art techniques is presented in the survey would help the researchers to organize their work on established lines.

Early Detection System for Gas Leakage and Fire in Smart Home Using IOT

Gas leakage is an important environmental, commercial and residential problem that threatens human health, safety and the environment. Traditional gas detection methods often lack the ability to track time and do not provide timely information, leading to accidents and damage. In this context, Internet of Things (IoT) technology has been found to provide a reliable solution for continuous monitoring and remote management of gas detection systems. This paper presents an IoT-based gas leak detection system to improve safety and reduce the risk of gas leakage. leak. The proposed system integrates gas sensors, microcontrollers, communication modules and cloud computing infrastructure to enable real-time monitoring, data analysis and remote monitoring. Gas detectors used in designated areas indicated the presence of hazardous gases, and data was collected directly from the 's central processing unit for analysis. The system provides rapid rescue and intervention by sending instant notifications via mobile applications in case of a gas leak. The main features of the proposed system are scalability, flexibility and interoperability, allowing integration with existing infrastructure and integration of different protocols. Additionally, the use of a cloud-based platform facilitates data storage, analysis and visualization, allowing partners to gain insight intogasleaks, processes and possible consequences

Sign Recognition and Voice Conversion Device for Dumb

This project proposes a comprehensive hardware setup for home automation and environmental monitoring. The system includes components such as a Bluetooth module for remote control, a voice module (APR9600) for audio feedback, an LCD for display purposes, relay modules for controlling electrical devices, bulbs with holders for lighting, a CPU fan for temperature regulation, speakers for audio output, and a 5V power supply for operation. The core functionality of the system revolves around the integration of flux sensors for environmental monitoring. When flux sensors detect specific conditions, predefined audio signals are played through the voice module. These conditions correspond to six distinct signs, facilitating real-time alerts and notifications. Moreover, the system offers remote control capabilities through Bluetooth connectivity, enabling users to manage lighting and fan operations conveniently. Additionally, temperature monitoring is implemented using a DHT11 sensor, triggering the activation of the CPU fan when temperature thresholds are exceeded. Furthermore, an LCD display enhances user interaction by providing visual feedback and status updates. The project utilizes Arduino UNO as the central processing unit and incorporates 12V 1A adapters for powering specific components. Connectors and power cables are utilized for seamless integration and electrical connectivity. Overall, this project presents an integrated solution for home automation and environmental monitoring, offering flexibility, convenience, and enhanced user experience.

Metal-phenolic networks-engineered polyimide aerogels for high-efficiency gold extraction and separation from e-waste mixture

Engineering adsorption materials that offer easy handling, efficient, and selective recovery of precious metals, such as gold, from electronic waste (e-waste) are of significant interest, especially given the escalating volume of discarded electronic devices. However, the harsh digested solvents (e.g., aqua regia), ultralow gold concentrations (several ppm), and competitive interference from various metals present significant challenges. Herein, we engineer a porous polyimide (PI) aerogel with chemically reductive, mechanically resilient, and environmentally recyclable capabilities, using biomass-derived metal-phenolic networks (MPNs), for highly selective and efficient gold extraction and separation. The resulting aerogels show a high adsorption efficiency (∼97.4 %), selective extraction of gold ions from 12 metal ions in simulated leached solutions, and superior purity (∼100 %) in the reduced gold particles. The gold ions are reduced, nucleate, and grow into large agglomeration (∼12 μm) as observed in experiments and confirmed by density functional theory (DFT) calculations, facilitating their separation from the aerogels without the need for complex processes (e.g., membrane separation). On application to a central processing unit (CPU), our aerogels effectively extracted > 96 % of gold ions even at ultralow concentrations of 3.6 ppm in aqua regia, generating at least 66 times more profit compared to the cost (profit/cost = ∼52.9/∼0.8 $⋅g−1). These results demonstrated that MPNs-engineered aerogels are promising economical and practical adsorbents for gold recovery in e-waste.

The Role of Inflammasome in Abdominal Aortic Aneurysm and Its Potential Drugs.

Abdominal aortic aneurysm (AAA) has been recognized as a serious chronic inflammatory degenerative aortic disease in recent years. At present, there is no other effective intervention except surgical treatment for AAA. With the aging of the human population, its incidence is increasing year by year, posing a serious threat to human health. Modern studies suggest that vascular chronic inflammatory response is the core process in AAA occurrence and development. Inflammasome, a multiprotein complex located in the cytoplasm, mediates the expression of various inflammatory cytokines like interleukin (IL)-1β and IL-18, and thus plays a pivotal role in inflammation regulation. Therefore, inflammasome may exert a crucial influence on the progression of AAA. This article reviews some mechanism studies to investigate the role of inflammasome in AAA and then summarizes several potential drugs targeting inflammasome for the treatment of AAA, aiming to provide new ideas for the clinical prevention and treatment of AAA beyond surgical methods.

Learning with Asynchronous Labels

Learning with data streams has attracted much attention in recent decades. Conventional approaches typically assume that the feature and label of a data item can be timely observed at each round. In many real-world tasks, however, it often occurs that either the feature or the label is observed firstly while the other arrives with delay. For instance, in distributed learning systems, a central processor collects training data from different sub-processors to train a learning model, whereas the feature and label of certain data items can arrive asynchronously due to network latency. The problem of learning with asynchronous feature or label in streams encompasses many applications but still lacks sound solutions. In this paper, we formulate the problem and propose a new approach to alleviate the negative effect of asynchronicity and mining asynchronous data streams. Our approach carefully exploits the timely arrived information and builds an online ensemble structure to adaptively reuse historical models and instances. We provide the theoretical guarantees of our approach and conduct extensive experiments to validate its effectiveness.

Rapid calculation for avalanche maps by GPGPU-based snow avalanche model

The required time for producing snow avalanche maps is influenced by computation speed of simulations. Commonly, integrating terrain assessment with dynamic flow simulation aids in mapping dangerous areas for human and structural threats. This approach enables the evaluation of avalanche paths, as well as the assessment of flow rate and thickness during avalanche movement. However, the substantial computational cost of the simulation results in long calculation times when using the Central Processing Unit (CPU). In this study, a new rapid snow avalanche simulator was developed by applying massively parallel computation with the General-Purpose computing on Graphics Processing Unit (GPGPU) technique. By avoiding slower data transfer and utilizing faster memory, computational speed could be accelerated up to 80 times faster than conventional simulation using a CPU. Additionally, the rapid calculation models were validated based on the Mt. Nasu event in 2017, and pilot studies of the avalanche map of Mt. Nasu in Japan demonstrated the usefulness of the developed model for vulnerability evaluation. A total of 123 simulations were conducted for each susceptible source area, and all simulations were completed within only 6.5 h. This high-performance calculation can significantly reduce the time cost of producing and expanding conventional avalanche maps.

FPGA-base object tracking: integrating deep learning and sensor fusion with Kalman filter

This research presents an integrated approach for object detection and tracking in autonomous perception systems, combining deep learning techniques for object detection with sensor fusion and field programmable gate array (FPGA-based) hardware implementation of the Kalman filter. This approach is suitable for applications like autonomous vehicles, robotics, and augmented reality. The study explores the seamless integration of pre-trained deep learning models, sensor data from a depth camera, real-sense D435, and FPGA-based Kalman filtering to achieve robust and accurate 3D position and 2D size estimation of tracked objects while maintaining low latency. The object detection and feature extraction are implemented on a central processing unit (CPU), and the Kalman filter sensor fusion with universal asynchronous receiver transmitter (UART) communication is implemented on a Basys 3 FPGA board that performs 8 times faster compared to the software approach. The experimental result provides the hardware resource utilization of about 29% of look-up tables, 6% of lookup table RAMs (LUTRAM), 15% of Flip-flops, 32% of Block-RAM, 38% of DSP blocks operating at 100 MHz, and 230400 baud rates for the UART. The whole FPGA design executes at 2.1 milliseconds, the Kalman filter executes at 240 microseconds, and the UART at 1.86 milliseconds.

Model Predictive Control With Stability Guarantee for Second-Order DC/DC Converters

This paper proposes a continuous-control-set model-predictive control (CCS-MPC) designed explicitly for second-order dc/dc converters such as the boost, buck, buck-boost, and non-inverting buck-boost converters. These converters share a bilinear dynamic structure that allows designing a generalized control. In contradistinction to the conventional finite-control-set approach, the proposed control uses a pulse-width modulation that allows a continuous control set with constant switching frequency. The modulation index's saturation is considered an inequality constraint in the optimization model. The proposed control guarantees stability in the sense of Lyapunov. This property is proven by using the passive structure of these converters. Experimental results on the four converters mentioned above show the superior performance of the proposed control in terms of robustness, offset, computation time in the central processing unit and, of course, stability.

Actuator Surface Modeling of Rotors at the Ship–Helicopter Dynamic Interface

The aerodynamics of the ship–helicopter dynamic interface play an important role in determining ship–helicopter operating limits. A review of prior studies, in conjunction with experimental work, indicates that a time-accurate rotor model and a physics-based turbulence-resolving flow solver with two-way coupling between these components are required to accurately predict rotor–airwake and rotor–rotor interactions. Here, a recently developed actuator surface model is validated against two further multirotor cases and employed for two large-scale ship–helicopter dynamic interfaces with up to three concurrent rotorcraft and five rotors. Simulations of the Sikorsky X2 rotor with significant rotor–rotor interaction match resolved unsteady rotor loading well using 1.2% of the computational effort of resolved blade calculations. Computations of the outwash from a Boeing CH-47D in hover gave results within experimental error bars for most azimuths, where discrepancies may be attributed to the estimated trim and division of rotor loading. Simulations of the dynamic interface between the Simple Frigate Shape 1 and a hovering UH-60a were presented for three wind-over-deck angles and three positions for 40 knots of wind speed. The computed thrust and root mean square thrust coefficients in the closed-loop pilot control band are presented and demonstrated to have an to have an expected dependence on immersion in the ship airwake. Finally, a large-scale dynamic interface computation was performed with two CH-47Ds and one UH-60a operating concurrently over the deck of the landing helicopter assault ship at 40 knots of wind speed and 0° and 30° angle. Results highlighted the importance of understanding rotor-wake/airwake interactions dependent on rotorcraft type and position and wind-over-deck. The computation provided unsteady thrust and torque for each of the five rotors from 60 s of data using just 11,424 central processing unit hours on a grid of 19.5×106 points. These simulations show that hybrid Navier–Stokes simulations employing actuator line or surface methods are capable of providing high-fidelity, time-accurate predictions of rotor loads at the ship–helicopter dynamic interface at substantially lower cost.