Science topics: Computer Science and EngineeringComputer Architecture
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Computer Architecture - Science topic
In computer science and engineering, computer architecture is the practical art of defining the structure and relationship of the subcomponents of a computer. As in designing the architecture of buildings, architecture can comprise many levels of information. The highest level of the definition conveys the concepts implement, whereas in building architecture this over-view is normally visual, computer architecture is primarily logical, positing a conceptual system that serves a particular purpose. In both instances (building and computer), many levels of detail are required to completely specify a given implementation. As in building architecture, some of these details are often implied as common practice.
Publications related to Computer Architecture (10,000)
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This article presents a comprehensive analysis of serverless computing architecture for modern payment gateway systems, addressing the challenges of scalability, security, and cost-efficiency in financial transaction processing. We propose a novel architectural framework that leverages event-driven serverless functions for core payment operations w...
Spintronic devices -based on magnetic solitons, such as the domain wall motion and the skyrmions, have shown a significant potential for applications in energy-efficient data storage and beyond CMOS computing architectures. Based on the magnetic multilayer hetero-structures, we propose a magnetic skyrmion-magnetic tunnel junction device structure,...
This study investigates the computational properties of ZnO colloids in combination with proteinoid microspheres within an unconventional computing framework. We propose a method for creating flexible and fault-tolerant logic gates utilising this colloidal system. The colloidal matrix receives binary strings with an electrical impulse representing...
In this article, we introduce a novel approach to achieving lightweight device authentication through the use of a low-complexity Convolutional Neural Network (CNN). In our work, we improve the False Authentication Rate (FAR) by transforming the standard CNN into a Bayesian CNN (BCNN or BNN). This transformation enables the use of probabilistic mod...
The pursuit of optimal neural network architectures is foundational to the progression of Neural Architecture Search (NAS). However, the existing NAS methods suffer from the following problem using traditional search strategies, i.e., when facing a large and complex search space, it is difficult to mine more effective architectures within a reasona...
Artificial neural networks have advanced due to scaling dimensions, but conventional computing struggles with inefficiencies due to memory bottlenecks. In-memory computing architectures using memristor devices offer promise but face challenges due to hardware non-idealities. This work proposes layer ensemble averaging—a hardware-oriented fault tole...
Brain-inspired computing architectures attempt to emulate the computations performed in the neurons and the synapses in human brain. Memristors with continuously tunable resistances are ideal building blocks for artificial synapses. Through investigating the memristor behaviors in a La0.7Sr0.3MnO3/BaTiO3/La0.7Sr0.3MnO3 multiferroic tunnel junction,...
Preparing the ground state of a local Hamiltonian is a crucial problem in understanding quantum many-body systems, with applications in a variety of physics fields and connections to combinatorial optimization. While various quantum algorithms exist which can prepare the ground state with high precision and provable guarantees from an initial appro...
Dissociated neuronal cultures provide a simplified yet effective model system for investigating self-organized prediction and information processing in neural networks. This review consolidates current research demonstrating that these in vitro networks display fundamental computational capabilities, including predictive coding, adaptive learning,...
Movable single atoms have drawn significant attention for their potential as flying quantum memory in non-local, dynamic quantum computing architectures. However, when dynamic optical tweezers are employed to control atoms opto-mechanically, conventional methods such as adiabatic controls and constant jerk controls are either inherently slow or ind...
The urgent penetration of the Industrial Internet of Things (IIoT) in industrial sectors has fostered unmatched benefits in terms of efficiency, productivity, and overall performance. Although current security frameworks already offer a sound level of protection, they do not adequately scale to the growing diversity and number of devices integrated...
Conventional computing architectures are not suited to meet the unique workload requirements of artificial intelligence and deep learning, which has sparked a growing interest in memory-centric computing. One primary challenge in this field is sneak path current in memory devices, which degrades data storage and reliability. Another critical issue...
In this work we present a novel implementation of delay line free reservoir computing based on state-of-the-art photonic technologies, which exploits chaotic optical frequency comb formation in optical microresonator as the nonlinear reservoir. Our solution leverages the high resonator Q-factor both for memory and for enhancing high dimensional non...
This article presents a novel architectural approach for optimizing latency in critical care monitoring systems through the integration of advanced computing technologies. The implemented system combines in-memory data management using Redis, strategically distributed edge processing nodes, and optimized streaming protocols to achieve significant i...
A memristor, memory resistor, is a two-terminal nano device that can be made as thin as a single atom thick that has become of tremendous interest for its potential to revolutionize electronics, computing, computer architecture, and neuromorphic engineering. This thesis encompasses two major parts containing original contributions, (Part I) modelli...
Two-dimensional (2D) polar magnets have received considerable attention due to their intrinsic ability to host Dzyaloshinskii–Moriya interaction (DMI), which is crucial for generating topological spin textures such as skyrmions and bimerons. The ability to switch between skyrmions and bimerons is considered to be important for developing future com...
Timely pathogen surveillance and reporting is essential for effective public health guidance. Web dashboards have become a key tool for communicating public health information to stakeholders, health care workers, and the broader community. Over the SARS-CoV-2 pandemic, wastewater surveillance has increasingly been incorporated into public health w...
Rapid urbanization presents significant challenges in energy consumption, noise control, and environmental sustainability. Smart cities aim to address these issues by leveraging information technologies to enhance operational efficiency and urban liveability. In this context, urban sound recognition supports environmental monitoring and public safe...
Today’s enterprise computing architectures are characterized by a complex memory hierarchy: different application requirements in terms of latency, bandwidth, persistence, and access pattern, as well as characteristics of available memory and storage technology require combining different technologies. Building highly efficient data management and...
In response to the challenges posed by traditional computing architectures in handling big data and AI demands, neuromorphic computing has emerged as a promising alternative inspired by the brain's efficiency. This study focuses on three-terminal synaptic transistors utilizing graphene and P(VDF-TrFE) to achieve dynamic reconfigurability between ex...
We propose a novel approach to generate, protect, and control Gottesman-Kitaev-Preskill (GKP) qubits. It employs a microwave frequency comb parametrically modulating a Josephson circuit to enforce a dissipative dynamics of a high-impedance circuit mode, autonomously stabilizing the finite-energy GKP code. The encoded GKP qubit is robustly protected...
Optoelectronic synapse devices (OESDs) inspired by human visual systems enable to integration of light sensing, memory, and computing functions, greatly promoting the development of in‐sensor computing techniques. Herein, dual‐mode integration of bipolar response photodetectors (PDs) and artificial optoelectronic synapses based on ZnO/SnSe heteroju...
Image loading represents a critical bottleneck in modern machine learning pipelines, particularly in computer vision tasks where JPEG remains the dominant format. This study presents a systematic performance analysis of nine popular Python JPEG decoding libraries on different computing architectures. We benchmark traditional image processing librar...
Distributed computing has emerged as a transformative paradigm that fundamentally reshapes our understanding of computational infrastructure. This comprehensive analysis explores the intricate landscape of modern distributed systems, examining the critical challenges and innovative solutions that drive technological evolution. By exploring the comp...
This article comprehensively analyzes the challenges and solutions in deploying real-time machine learning recommendation systems at scale. The article examines the critical trade-offs between model complexity, inference latency, and system scalability that impact modern recommendation architectures. The article investigates three primary dimension...
The advent of 6G networks and beyond calls for innovative paradigms to address the stringent demands of emerging applications, such as extended reality and autonomous vehicles, as well as technological frameworks like digital twin networks. Traditional cloud computing and edge computing architectures fall short in providing their required flexibili...
Welcome to "C Programming Essentials: A Practical Guide for Developers." This note is designed to be your companion on the journey to mastering the C programming language—an essential skill for any aspiring or seasoned developer. Whether you are a student taking your first steps into the world of programming or a professional looking to enhance you...
Memory compression is an important approach in computer architecture for decreasing memory footprint and improving system performance. In this paper, we use C/C++ to develop a current memory compression algorithm; the Global Bases Delta Immediate (GBDI) algorithm, which was proposed at HPCA'2022. By using global bases and enabling deltas within the...
Os avanços tecnológicos têm levado a indústria de computadores ao desenvolvimento de novas arquiteturas de computadores, mais velozes e robustas, denominadas de arquiteturas paralelas, compostas por computadores com múltiplos processadores, que colaboram entre si na solução de problemas complexos, explorando diferentes níveis de paralelismo. Uma al...
This paper presents a conceptual framework, SocialMapReduce, that reimagines distributed computing through the lens of human social dynamics. We propose that by integrating cooperative and competitive social behaviors into multi-agent AI systems, we can create more adaptive and efficient distributed architectures. The framework challenges the tradi...
The increasing demand for processing large volumes of data for machine learning (ML) models has pushed data bandwidth requirements beyond the capability of traditional von Neumann architecture. In-memory computing (IMC) has recently emerged as a promising solution to address this gap by enabling distributed data storage and processing at the micro-...
Recently, serverless computing has gained recognition as a leading cloud computing method. Providing a solution that does not require direct server and infrastructure management, this technology has addressed many traditional model problems by eliminating them. Therefore, operational complexity and costs are reduced, allowing developers to concentr...
With the rapidly increasing rate of microlensing planet detections, microlensing modeling software faces significant challenges in computation efficiency. Here, we develop the Twinkle code, an efficient and robust binary-lens modeling software suite optimized for heterogeneous computing devices, especially GPUs. Existing microlensing codes have the...
Ising annealer is a promising quantum-inspired computing architecture for combinatorial optimization problems. In this paper, we introduce an Ising annealer based on the Hamiltonian Monte Carlo, which updates the variables of all dimensions in parallel. The main innovation is the fusion of an approximate gradient-based approach into the Ising annea...
The future of the energy sector is in the digitalization of energy systems, with Digital Twin technology emerging as a key contributor. Integration of Digital Twin, a virtual representation that functions as a physical object’s real-time digital equivalent, with advanced technologies, such as 5G, Internet of Things, Machine Learning/Artificial Inte...
This article examines the transformative impact of artificial intelligence (AI) and machine learning (ML) integration in cloud computing architectures across enterprise operations. Through an analysis of 250+ enterprise deployments, we demonstrate significant improvements including a 42% reduction in operational costs and 53% enhancement in process...
Bayesian optimisation (BO) protocols grounded in active learning (AL) principles have gained significant recognition for their ability to efficiently optimize black-box objective functions. This capability is critical for advancing autonomous and high-throughput materials design and discovery processes. However, the application of these protocols i...
Recently, there is a great interest in smart parking application. Theses applications are enhanced by a vehicular ad-hoc network, which helps drivers find and reserve satiable packing spaces for a period of time ahead of time. Named Data Networking (NDN) is a future Internet architecture that benefits vehicular ad-hoc networks because of its clean-...
High‐density bio‐electrolyte‐gated synaptic transistors (BEGTs) array are promising for constructing neuromorphic computing architectures. Due to the bulk ion conductivity and the crack sensitivity of the electrolyte film, patterning the electrolyte is an indispensable route to prevent spatial crosstalk and improve the flexibility of the device arr...
We propose a modification to the transpiler of a quantum computer to safeguard against side-channel attacks aimed at learning information about a quantum circuit. We demonstrate that if it is feasible to shield a specific subset of gates from side-channel attacks, then it is possible to conceal all information in a quantum circuit by transpiling it...
Optical edge detection is a crucial optical analog computing method in fundamental artificial intelligence, machine vision, and image recognition, owing to its advantages of parallel processing, high computing speed, and low energy consumption. Field‐of‐view‐tunable edge detection is particularly significant for detecting a broader range of objects...
Solving optimization problems is a highly demanding workload requiring high-performance computing systems. Optimization solvers are usually difficult to parallelize in conventional digital architectures, particularly when stochastic decisions are involved. Recently, analog computing architectures for accelerating stochastic optimization solvers hav...
Image classification using deep learning has gained significant attention, with various datasets available for benchmarking algorithms and pre-trained models. This study focuses on the Microsoft ASIRRA dataset, renowned for its quality and benchmark standards, to compare different pre-trained models. Through experimentation with optimizers, loss fu...
This paper presents research that integrates low-altitude CubeSats for signal telemetry while enhancing these CubeSats for the acquisition of imaging data. The objective of this study is to improve a CubeSat by incorporating a radar system, Lidar, and a fisheye lens camera to capture high-resolution imaging data from long-range signals. This proces...
Introduction
Detecting declines in cognitive function is a critical global health concern, highlighting the need for timely identification to implement effective intervention strategies. This study investigates the potential of blood‐based biomarkers as accurate and non‐invasive measures of cognitive function. We developed a novel deep learning arc...
With the exponential rise in the use of cloud services, smart devices, and IoT devices, advanced cyber attacks have become increasingly sophisticated and ubiquitous. Furthermore, the rapid evolution of computing architectures and memory technologies has created an urgent need to understand and address hardware security vulnerabilities. In this pape...
Edge, fog, and cloud computing provide complementary capabilities to enable distributed processing of IoT data. This requires offloading mechanisms, decision-making mechanisms, support for the dynamic availability of resources, and the cooperation of available nodes. This paper proposes a novel 3-tier architecture that integrates edge, fog, and clo...
Application-specific quantum computers offer the most efficient means to tackle problems intractable by classical computers. Realizing these architectures necessitates a deep understanding of quantum circuit properties and their relationship to execution outcomes on quantum devices. Our study aims to perform for the first time a rigorous examinatio...
This paper addresses the increasing demand for efficient and scalable streaming service applications within the context of edge computing, utilizing NVIDIA Jetson Xavier NX hardware and Docker. The study evaluates the performance of DeepStream and Simple Realtime Server, demonstrating that containerized applications can achieve performance levels c...
Welcome to "Foundations of Computer Architecture: Principles and Design." This textnote is designed to serve as a comprehensive introduction to the field of computer architecture, providing a solid foundation for students, educators, and professionals who wish to understand the inner workings of modern computer systems. As the landscape of technolo...
The field of astrophysics has long sought computational tools capable of harnessing the power of modern GPUs to simulate the complex dynamics of astrophysical phenomena. The Kratos Framework, a novel GPU-based simulation system designed to leverage heterogeneous computing architectures, is introduced to address these challenges. Kratos offers a fle...
Integrating cloud and edge computing architectures in telecommunications networks significantly advances service delivery and resource management. This article presents a comprehensive analysis of hybrid cloud-edge ecosystems through the lens of the TANDEM framework, examining its role in orchestrating distributed computing resources across telecom...
We introduce QuArch, a dataset of 1500 human-validated question-answer pairs designed to evaluate and enhance language models' understanding of computer architecture. The dataset covers areas including processor design, memory systems, and performance optimization. Our analysis highlights a significant performance gap: the best closed-source model...
In-memory analog matrix computing (AMC) with resistive random-access memory (RRAM) represents a highly promising solution that solves matrix problems in one step. However, the existing AMC circuits each have a specific connection topology to implement a single computing function, lack of the universality as a matrix processor. In this work, we desi...
Bayesian approaches to decision trees (DTs) using Markov Chain Monte Carlo (MCMC) samplers have recently demonstrated state-of-the-art accuracy performance when it comes to training DTs to solve classification problems. Despite the competitive classification accuracy, MCMC requires a potentially long runtime to converge. A widely used approach to r...
Small registers of spin qubits in silicon can exhibit hour-long coherence times and exceeded error-correction thresholds. However, their connection to larger quantum processors is an outstanding challenge. To this end, spin qubits with optical interfaces offer key advantages: they can minimize the heat load and give access to modular quantum comput...
A new technique for generating true random numbers by using the ADPLL (All Digital Phase Locked Loop)-based multiple ring oscillator TRNG (MURO-TRNG) is discussed in this paper. The proposed ADPLL-based MURO-TRNG contains 10 ring oscillators, 1 conventional ADPLL, 11 sampling DFFs, 1 XOR gate, and an XOR corrector-based post-processing circuit. Rin...
Image completion refers to the problem of recovering the missing, corrupted or obscured entries in image data. In this paper, we consider the problem in the remote sensing domain, where regions of an image are missing due to difficulties such as cloud cover, sensor failures or partial sensor coverage. Where previous work in this field generally fal...
While the Karatsuba algorithm reduces the complexity of large integer multiplication, the extra additions required minimize its benefits for smaller integers of more commonly-used bitwidths. In this work, we propose the extension of the scalar Karatsuba multiplication algorithm to matrix multiplication, showing how this maintains the reduction in m...
Implementation of Convolutional Neural Networks (CNNs) on edge devices require reduction in computational complexity. Leveraging optimization techniques or approximate computing techniques can reduce the overhead associated with hardware implementation. In this paper, we propose a modular pipelined Feedforward CNN Hardware Accelerator (FHA) and a n...
Computer vision is becoming an increasingly vital field, offering significant opportunities for real-world applications. Object counting is one of its core aspects, with increasing utilization across scientific fields involving objects of varying sizes. Traditional counting methods, however, face challenges in dense scenarios, as they are often ine...
Multispectral imaging and deep learning have emerged as powerful tools supporting diverse use cases from autonomous vehicles to agriculture, infrastructure monitoring and environmental assessment. The combination of these technologies has led to significant advancements in object detection, classification, and segmentation tasks in the non-visible...
In this paper, we present a study on copula-driven learning techniques for physical layer authentication (PLA) in wireless communication, using data from multiple modalities. The collective multimodal data is considered as an attribute vector, which is used as a test statistic for the underlying multi-level hypothesis testing problem of PLA. We con...
Demands for secure, ubiquitous, and always-available connectivity have been identified as the pillar design parameters of the next generation radio access networks (RANs). Motivated by this, the current contribution introduces a network architecture that leverages blockchain technologies to augment security in RANs, while enabling dynamic coverage...
The industrial sector has suffered changes over time until it came to the fourth phase, called Industry 4.0. Despite developments in this area, some companies are failing to keep up, which makes them less competitive with the rest. Thus, the presented work proposes a system that can support a Digital Twin of Stone Factory, a sector that has not bee...