Central Processing Unit Research Articles

A Survey on Design Space Exploration Approaches for Approximate Computing Systems

Approximate Computing (AxC) has emerged as a promising paradigm to enhance performance and energy efficiency by allowing a controlled trade-off between accuracy and resource consumption. It is extensively adopted across various abstraction levels, from software to architecture and circuit levels, employing diverse methodologies. The primary objective of AxC is to reduce energy consumption for executing error-resilient applications, accepting controlled and inherently acceptable output quality degradation. However, harnessing AxC poses several challenges, including identifying segments within a design amenable to approximation and selecting suitable AxC techniques to fulfill accuracy and performance criteria. This survey provides a comprehensive review of recent methodologies proposed for performing Design Space Exploration (DSE) to find the most suitable AxC techniques, focusing on both hardware and software implementations. DSE is a crucial design process where system designs are modeled, evaluated, and optimized for various extra-functional system behaviors such as performance, power consumption, energy efficiency, and accuracy. A systematic literature review was conducted to identify papers that ascribe their DSE algorithms, excluding those relying on exhaustive search methods. This survey aims to detail the state-of-the-art DSE methodologies that efficiently select AxC techniques, offering insights into their applicability across different hardware platforms and use-case domains. For this purpose, papers were categorized based on the type of search algorithm used, with Machine Learning (ML) and Evolutionary Algorithms (EAs) being the predominant approaches. Further categorization is based on the target hardware, including Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), general-purpose Central Processing Units (CPUs), and Graphics Processing Units (GPUs). A notable observation was that most studies targeted image processing applications due to their tolerance for accuracy loss. By providing an overview of techniques and methods outlined in existing literature pertaining to the DSE of AxC designs, this survey elucidates the current trends and challenges in optimizing approximate designs.

Inferring TLB Configuration with Performance Tools

Modern computing systems are primarily designed for maximum performance, which inadvertently introduces vulnerabilities at the micro-architecture level. While cache side-channel analysis has received significant attention, other Central Processing Units (CPUs) components like the Translation Lookaside Buffer (TLB) can also be exploited to leak sensitive information. This paper focuses on the TLB, a micro-architecture component that is vulnerable to side-channel attacks. Despite the coarse granularity at the page level, advancements in tools and techniques have made TLB information leakage feasible. The primary goal of this study is not to demonstrate the potential for information leakage from the TLB but to establish a comprehensive framework to reverse engineer the TLB configuration, a critical aspect of side-channel analysis attacks that have previously succeeded in extracting sensitive data. The methodology involves detailed reverse engineering efforts on Intel CPUs, complemented by analytical tools to support TLB reverse engineering. This study successfully reverse-engineered the TLB configurations for Intel CPUs and introduced visual tools for further analysis. These results can be used to explore TLB vulnerabilities in greater depth. However, when attempting to apply the same methodology to the IBM Power9, it became clear that the methodology was not transferable, as mapping functions and performance counters vary across different vendors.

Inside the Mind of the Self-Deceiver: Foundational Issues in Relation to Human Defences and “the Unconscious”

AbstractThere are many foundational questions involved in understanding our defences. Are these manoeuvres unconscious processes to which we fall prey, as psychoanalytic metapsychology suggests? Or are they the product of a conscious and motivated strategy initiated by the individual? I argue for the latter. The concept of “unconscious defence” lacks explanatory power as it initiates an endless cycle of defence mechanisms. Alternatively, focus is directed towards consciousness and the pre-reflective moment when the ego evades addressing troubling content of consciousness. Existential philosophers such as Sartre (Being and nothingness, Methuen & Co, 1943) and Fingarette (Self-deception, Routledge & Kegan Paul, 1969) are used as stepping stones in making this “Copernican” shift in perspective, and core affective processes are introduced as the organismic centre of rotation behind our defences. Core affective processes play an important role in the toning of our emotions and moods, and I argue that they also play a leading role in the activation of our defences. Finally, ethical issues raised by self-deception in psychological practice are touched upon. Should the truth always be revealed, or should psychologists occasionally engage in cost-benefit analysis?

Unveiling the nature of peer development groups: A systematic review, conceptual framework, and research pathways

SummaryThis research provides a systematic review of the literature on peer development groups (PDGs), a promising yet underexplored tool for individual and leadership development. Despite the growing interest in PDGs from both scholars and practitioners, the field remains fragmented, with inconsistent terminologies and limited theoretical grounding. This review seeks to clarify the core components, boundary conditions, inputs, processes, and outcomes of effective PDGs in professional settings, addressing the confusion and gaps in existing research. By adopting an adjudication approach, we provide a comprehensive synthesis of the evidence regarding the defining characteristics of PDGs and their impacts on individual development. We propose a new, scholarly definition of PDGs as organized small groups consisting of members of perceived similar status and roles who regularly meet to foster mutual growth by providing a supportive environment and a flexible agenda. We develop a conceptual framework that links key variables influencing PDG effectiveness and offers actionable insights for both researchers and practitioners. Our findings highlight opportunities for future research and suggest practical implications for implementing PDGs in organizational settings, ultimately contributing to a more cohesive and theoretically informed understanding of this valuable developmental practice.

Scalable O(log2n) Dynamics Control for Soft Exoskeletons

Robotic exoskeletons are being actively applied to support the activities of daily living (ADL) for patients with hand motion impairments. In terms of actuation, soft materials and sensors have opened new alternatives to conventional rigid body structures. In this arena, biomimetic soft systems play an important role in modeling and controlling human hand kinematics without the restrictions of rigid mechanical joints while having an entirely deformable body with limitless points of actuation. In this paper, we address the computational limitations of modeling large-scale articulated systems for soft robotic exoskeletons by integrating a parallel algorithm to compute the exoskeleton’s dynamics equations of motion (EoM), achieving a computation with O(log2n) complexity for the highly articulated n degrees of freedom (DoF) running on p processing cores. The proposed parallel algorithm achieves an exponential speedup for n=p=64 DoF while achieving a 0.96 degree of parallelism for n=p=256, which demonstrates the required scalability for controlling highly articulated soft exoskeletons in real time. However, scalability will be bounded by the n=p fraction.

Lower Energy Consumption in Multi-CPU Cell-Free Massive MIMO Systems

Under the ideal assumption of deploying only one central processing unit (CPU) in the entire system, cell-free (CF) systems can achieve significant macro-diversity gain, thereby providing uniformly reliable service to each user equipment (UE). However, due to limitations in system scalability and the feasibility of strict phase synchronization, CF systems require a multi-CPU setup and perform coherent transmission at a smaller scale. Moreover, conventional CF systems typically operate in time-division duplex (TDD) mode and utilize statistical channel state information (CSI) for downlink (DL) decoding, but the channel hardening effect is not significant. These factors reduce downlink spectral efficiency (SE) and increase DL transmission time, leading to higher energy consumption in CF systems. To address these issues, we introduce downlink channel estimation (DLCE) in multi-CPU CF systems and derive the approximate achievable DL SE. To reduce DL pilot overhead, we propose an uplink–pilot-reuse-constrained DL pilot allocation principle. Based on this principle, we develop a farthest distance pilot allocation (FDPA) algorithm to mitigate pilot contamination. In addition, leveraging the characteristics of the heuristic distributed power allocation algorithm, we propose two access point (AP) clustering algorithms: one based on CSI (BCSI) and the other based on coherent group size (BCGS). Simulation results indicate that the introduction of DLCE significantly improves DL SE in multi-CPU CF massive MIMO systems, while the proposed FDPA algorithm further enhances DL SE. The BCSI and BCGS algorithms also effectively improve DL SE and help reduce energy consumption. By combining DLCE, the FDPA algorithm, and the proposed AP clustering algorithms, the energy consumption of multi-CPU CF systems can be significantly reduced.

Role of N-Glycosylation in Gastrointestinal Cancers.

Gastrointestinal cancers pose a significant global health challenge. N-glycosylation modulates various cellular processes, including key cancer-related mechanisms. Elucidating its involvement in the onset and advancement of these cancers can offer critical insights for enhancing diagnostic and therapeutic approaches. This review outlines the core process of protein N-glycosylation and highlights its contribution to the progression of gastrointestinal cancers, encompassing cell proliferation, survival, invasion, metastasis, and immune evasion, mainly through its impact on critical signaling pathways. Notably, aberrant N-glycosylation patterns have emerged as crucial biomarkers for the diagnosis and prognosis of various gastrointestinal cancers, providing the foundation for more personalized therapeutic approaches. Therapeutic strategies targeting N-glycosylation, such as glycosyltransferase inhibitors and glycoengineering, show significant promise in mitigating tumor aggressiveness and enhancing immune recognition. However, the clinical implementation of N-glycosylation biomarkers requires the standardization of glycosylation analysis techniques and solutions to challenges in sample processing and data interpretation. Future research efforts should concentrate on overcoming these obstacles to unlock the full potential of N-glycosylation in enhancing cancer management and advancing patient outcomes.

Computing Unit and Data Migration Strategy under Limited Resources: Taking Train Operation Control System as an Example

There are conflicts between the increasingly complex operational requirements and the slow rate of system platform upgrading, especially in the industry of railway transit-signaling systems. We attempted to address this problem by establishing a model for migrating computing units and data under resource-constrained conditions in this paper. By decomposing and reallocating application functions, optimizing the use of CPU, memory, and network bandwidth, a hierarchical structure of computing units is proposed. The architecture divides the system into layers and components to facilitate resource management. Then, a migration strategy is proposed, which mainly focuses on moving components and data from less critical paths to critical paths and ultimately optimizing the utilization of computing resources. Specifically, the test results suggest that the method can reduce the overall CPU utilization by 27%, memory usage by 6.8%, and network bandwidth occupation by 35%. The practical value of this study lies in providing a theoretical model and implementation method for optimizing resource allocation in scenarios where there is a gap between resource and computing requirements in fixed-resource service architectures. The strategy is compatible for distributed computing architectures and cloud/cloud–edge-computing architectures.

Analog Implementation of a Spiking System for Performing Arithmetic Logic Operations on Mixed‐Signal Neuromorphic Processors

In recent years, physical limitations in the integration of transistors in computers have forced the search for low‐computational‐power alternatives in hardware design. Although doubts may arise regarding the limit of the relationship between performance and power consumption in computers, these disappear when considering the brain, which is one of the most efficient computing systems. In this way, bioinspired applications try to benefit from the low‐power consumption present in the biological nervous system. Previous work has shown the feasibility of implementing spiking neural networks that operate in a Boolean manner on digital platforms, such as SpiNNaker, using basic logic gates and a spiking memory, which suggests the potential for constructing a low‐power consumption spiking computer. This work takes a first step in the implementation of a spiking central processing unit by developing an arithmetic logic unit, which is an essential block for instruction execution, demonstrating its correct operation on Dynap‐SE1. The results confirm the feasibility of using this Boolean approach on this platform, despite certain limitations in the number of inputs and operating frequencies of the blocks, and pave the way for the construction of a spiking computer.

Energy-Aware Register Allocation for VLIW Processors

AbstractThe efficiency of VLIW processors can be improved by reducing the energy consumption associated with accessing the register-file. This paper presents an energy-aware register allocation approach to reduce the dynamic energy consumption of a scheduled program by using an evolutionary algorithm approach and a register access transition energy model. Additionally, the influence of instruction scheduling on energy-aware register allocation is analyzed. By using the proposed energy-aware compiler backend on synthetic applications with random data in the register-file, the dynamic energy consumption of the total processor core for an exemplary VLIW processor can be reduced by up to $$\varvec{21\%}$$ 21 % compared to a heuristic register allocation. In real-world applications from the domain of hearing aids, energy consumption can be reduced by up to $$\varvec{42.7\%}$$ 42.7 % for single applications, and $$\varvec{17.6\%}$$ 17.6 % average across a range of applications.

Thiourea-linked covalent triazine frameworks enable efficient and selective gold recovery from e-waste leachate

The surging electronic waste (e-waste) has been stimulating the unremitting exploration about the advanced gold recycling technology, which is of great significance to alleviate the shortage of metal resources and minimize the negative environmental impact. Herein, the thiourea-linked covalent triazine frameworks (denoted as PbTu-CTFs) have been designed and constructed by the nucleophilic substitution between cyanuric chloride (CC) and 1,4-phenylene bis(thiourea) (named PbTu) for gold recovery from the actual e-waste leachate. Benefiting from the unique structural characteristics, including large surface areas, abundant porosity, excellent chemical stability, and high-density S, N coordinative sites, the fabricated PbTu-CTFs afford ultrahigh gold adsorption capacity up to 2255 mg/g and fast kinetics, as reflected by 99 % of Au recovery efficiency within 5 min. Meanwhile, satisfactory recyclability and regeneration are maintained after six rounds of consecutive adsorption/desorption tests. Furthermore, the resulting adsorbent is well competent for highly efficient and selective gold recovery from the acidic leaching solution of wasted central processing unit (CPU), exemplify its promising actual application potential.

Tensor power flow formulations for multidimensional analyses in distribution systems

In this paper, we present two multidimensional power flow formulations based on a fixed-point iteration (FPI) algorithm to efficiently solve hundreds of thousands of Power flows (PFs) in distribution systems. The presented algorithms are the base for a new TensorPowerFlow (TPF) tool and shine for their simplicity, benefiting from multicore Central processing unit (CPU) and Graphics processing unit (GPU) parallelization. We also focus on the mathematical convergence properties of the algorithm, showing that its unique solution is at the practical operational point. The proof is validated using numerical simulations showing the robustness of the FPI algorithm compared to the classical Newton–Raphson (NR) approach. In the case study, a benchmark with different PF solution methods is performed, showing that for applications requiring a yearly simulation at 1-minute resolution, the computation time is decreased by a factor of 164, compared to the NR in its sparse formulation. Finally, a set of applications is described, highlighting the potential of the proposed formulations over a wide range of analyses in distribution systems.

Design and implementation of a nondestructive testing system for magnetic field imaging based on machine learning

This study presents the design of a nondestructive testing (NDT) system for detecting metal defects using a magnetic field sensor array. The system integrates a hardware-software detection framework that includes parallel computation and processing cores, along with multiple anisotropic magnetoresistance (AMR) sensors. These AMR sensors capture subtle variations in the magnetic field, which serve as the foundation for defect identification. The system enables synchronized data acquisition from multiple AMR sensors, achieving multi-axis data fusion and parallel signal processing. Furthermore, the detection system gathers defect features to train an NDT system based on machine learning (ML). Subsequently, ML-generated defect imaging features are processed through an imaging framework, producing magnetic field imaging (MFI) for precise defect localization. The study tested six different metal samples with various defect sizes and types to validate the system. The results demonstrate the system’s ability to accurately detect and identify different defects, highlighting the high precision of magnetic field detection and the overall effectiveness of the proposed system for NDT.

Improved automated parallel implementation of GMM background subtraction on a multicore digital signal processor

<p><span>Scene segmentation is an essential step in a wide range of video processing applications, for instance, object recognition and tracking. The Gaussian mixture model (GMM) for background subtraction (BS) has gained widespread usage in scene segmentation, despite its known computational intensity. To tackle this challenge, we propose a practical solution to accelerate processing through a parallel implementation on an embedded multicore platform. In this paper, we present an improved automated parallel implementation of the GMM algorithm using the Orphan directive provided by open multiprocessing (OpenMP). Experimental assessments conducted on the eight cores of the C6678 digital signal processor (DSP) demonstrate significant advancements in parallel efficiency, particularly when handling high-resolution frames, including high-definition (HD) and full-HD resolutions. The achieved parallel efficiency surpasses the results obtained with classical OpenMP scheduling modes, encompassing dynamic, static, and guided approaches. Specifically, the parallel efficiency reaches approximately 82% for full-HD resolution frames and, 99.3% for low-resolution frames, respectively.</span></p>

Probing protoneutron stars with gamma-ray axionscopes

Axion-like particles (ALPs) coupled to nucleons can be efficiently produced in the interior of protoneutron stars (PNS) during supernova (SN) explosions. If these ALPs are also coupled to photons they can convert into gamma rays in the Galactic magnetic field. This SN-induced gamma-ray burst can be observable by gamma-ray telescopes like Fermi-LAT if the SN is in the field of view of the detector. We show that the observable gamma-ray spectrum is sensitive to the production processes in the SN core. In particular, if the nucleon-nucleon bremsstrahlung is the dominant axion production channel, one expects a thermal spectrum with average energy E a ≃ 50 MeV. In this case the gamma-ray spectrum observation allows for the reconstruction of the PNS temperature. In case of a sizable pion abundance in the SN core, one expects a second spectral component peaked at E a ≃ 200 MeV due to axion pionic processes. We demonstrate that, through a dedicated LAT analysis, we can detect the presence of this pionic contribution, showing that the detection of the spectral shape of the gamma-ray signal represents a unique probe of the pion abundance in the PNS.

VOLATILE ANTIDIABETIC PROPERTIES OF PIPER NIGRUM L. ETHANOL EXTRACT (ORIGINAL AND PLANT STEM CELL): NETWORK PHARMACOLOGY STUDY AND ANTIOXIDANT ACTIVITY

Objective: This study aims to identify and compare the active chemical components in the ethanol extracts of Piper nigrum L. (black pepper) plant and its callus and to investigate their potential roles in treating diabetes mellitus through Protein-Protein Interaction (PPI) analysis. Methods: Ethanol extracts were prepared from both the original black pepper plant and its callus. Chemical analysis identified key active substances, including piperine and β-D-Glucopyranoside, using Retention Times (RT). PPI investigations were conducted to determine the interactions involved in diabetes management. The antioxidant capacities of the extracts were assessed using IC50 values, and the biological processes and molecular functions related to diabetes treatment were evaluated. Results: Both the original plant and callus extracts contained active substances such as piperine (37.715%, RT: 28.1967) and β-D-Glucopyranoside (54.272%, RT: 16.5768). The primary biological processes identified were the P450 epoxygenase pathway and glycogen production. Additionally, the organic acid metabolic process and nucleosome core were implicated in the management of diabetes mellitus by the extracts. The main molecular functions predicted were p53 binding and cyclin. The antioxidant capacities of the extracts were moderate for the callus extract (IC50: 129.92±0.83) and poor for the original plant extract (IC50: 156.69±1.36). Conclusion: The study reveals that the ethanol extracts from the black pepper callus and the original plant possess distinct chemical profiles and mechanisms in treating diabetes mellitus. The callus extract demonstrates a more favorable antioxidant capacity compared to the original plant extract. Both extracts engage in similar biological processes but exhibit differences in their chemical composition and potential therapeutic pathways for diabetes management.

Evaluating the Core Processes of a Hybrid Trauma-Informed Acceptance and Commitment Therapy Intervention for College Students

Evaluating the Core Processes of a Hybrid Trauma-Informed Acceptance and Commitment Therapy Intervention for College Students

Automatic control algorithm for V2G frequency response mode of electric vehicle charging station

Abstract To solve the problem of excessive fluctuation of the V2G load of the electric vehicle charging pile, this paper proposes an automatic control algorithm for the V2G frequency response mode of the electric vehicle charging pile. The FPGA is used as the core processing unit to collect and process the V2G frequency data of the charging pile; the multiple linear regression model is used to predict the charging and discharging loads, and the automated two-layer control model is established; the sparrow search algorithm is used to optimize the weights and thresholds of the BP neural network to realize the control of the V2G frequency response mode. The experimental results show that the load prediction error of this method is small. After the automatic control, the load fluctuation of the charging pile is small, and the electric vehicle charging pile’s V2G frequency response control effect is better.

IoT Based Sign to Speech Converter System

The idea introduces a system for facilitating communication between the deaf and the non-signing community through a wearable device called IoT based Sign-to-Speech Converter System. The System consist of glove & it utilizes Internet of Things (IoT) technology to bridge the communication gap by translating sign language (gestures) into spoken language in real-time. The core functionality of the system is augmented by seamless integration with an Android application, enhancing user experience and accessibility. The proposed system leverages a combination of sensors embedded within the glove to capture intricate hand movements and gestures commonly used in sign language. These data are transmitted wirelessly to a central processing unit, to interpret the gestures and generates corresponding spoken language output. The Android application serves as a user interface, allowing for customization of settings and additional features such as text-based communication. Effectiveness and practicality of the proposed system have been demonstrated, opening-up new avenues for inclusive communication and empowerment of the deaf community.

Exploring Food Processing in Natural Science Education: Practical Applications and Pedagogical Techniques

Food processing is an essential link in the chain that connects agricultural production with consumer nutrition and safety. This paper attempts to discuss how effectively food processing can be integrated into the curriculum of natural sciences to further the understanding of students about scientific principles through practical applications. Discussion of relevance to food processing prepares the ground for biology, chemistry, and environmental science, focusing on such core processes as fermentation, pasteurization, and conservation. This paper outlines how reviewing successful strategies and case studies, concept concretization of science is realized through practical activities and interdisciplinary projects, and larger goals are achieved in education for sustainability, food safety, and healthier eating. Besides, it underscores the place of technology and innovation through digital tools and virtual simulation in making the lessons interactive for the students in a career-oriented manner. The article concludes with the following practical suggestions for educators and advocates of the partnership among schools, industry, and community toward an enriched education preparing students with vocational skills.