4,989 research outputs found

    Cooperative end-edge-cloud computing and resource allocation for digital twin enabled 6G industrial IoT

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    End-edge-cloud (EEC) collaborative computing is regarded as one of the most promising technologies for the Industrial Internet of Things (IIoT). It offers effective solutions for managing computationally intensive and delay-sensitive tasks efficiently. Indeed, achieving intelligent manufacturing in the context of 6G networks requires the development of efficient resource scheduling schemes. However, improving the quality of service and resource management in the face of challenges like time-varying physical operating environments of IIoT, task heterogeneity, and the coupling of different resource types is undoubtedly a complex task. In this work, we propose a digital twin (DT) assisted EEC collaborative computing scheme, where DT is utilized to monitor the physical operating environment in real-time and determine the optimal strategy, and the potential deviation between the real values and DT estimates is also considered. We aim to minimize the system cost by optimizing device association, offloading mode, bandwidth allocation, and task split ratio. Our optimization is constrained by the maximum tolerable latency of the task while considering both latency and energy consumption. To solve the collaborative computation and resource allocation (CCRA) problem in the EEC, we propose an algorithm with DT based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG), where each user end (UE) in DT operates as an independent agent to determine the optimum offloading decision autonomously. Simulation results demonstrate the effectiveness of the proposed scheme, which can significantly improve the task success rate compared to benchmark schemes, while reducing the latency and energy consumption of task offloading with the assistance of DT

    Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

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    This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Towards a centralized multicore automotive system

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    Today’s automotive systems are inundated with embedded electronics to host chassis, powertrain, infotainment, advanced driver assistance systems, and other modern vehicle functions. As many as 100 embedded microcontrollers execute hundreds of millions of lines of code in a single vehicle. To control the increasing complexity in vehicle electronics and services, automakers are planning to consolidate different on-board automotive functions as software tasks on centralized multicore hardware platforms. However, these vehicle software services have different and contrasting timing, safety, and security requirements. Existing vehicle operating systems are ill-equipped to provide all the required service guarantees on a single machine. A centralized automotive system aims to tackle this by assigning software tasks to multiple criticality domains or levels according to their consequences of failures, or international safety standards like ISO 26262. This research investigates several emerging challenges in time-critical systems for a centralized multicore automotive platform and proposes a novel vehicle operating system framework to address them. This thesis first introduces an integrated vehicle management system (VMS), called DriveOSℱ, for a PC-class multicore hardware platform. Its separation kernel design enables temporal and spatial isolation among critical and non-critical vehicle services in different domains on the same machine. Time- and safety-critical vehicle functions are implemented in a sandboxed Real-time Operating System (OS) domain, and non-critical software is developed in a sandboxed general-purpose OS (e.g., Linux, Android) domain. To leverage the advantages of model-driven vehicle function development, DriveOS provides a multi-domain application framework in Simulink. This thesis also presents a real-time task pipeline scheduling algorithm in multiprocessors for communication between connected vehicle services with end-to-end guarantees. The benefits and performance of the overall automotive system framework are demonstrated with hardware-in-the-loop testing using real-world applications, car datasets and simulated benchmarks, and with an early-stage deployment in a production-grade luxury electric vehicle

    Hyperspectral Image Classification: An Analysis Employing CNN, LSTM, Transformer, and Attention Mechanism

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    Hyperspectral images contain tens to hundreds of bands, implying a high spectral resolution. This high spectral resolution allows for obtaining a precise signature of structures and compounds that make up the captured scene. Among the types of processing that may be applied to Hyperspectral Images, classification using machine learning models stands out. The classification process is one of the most relevant steps for this type of image. It can extract information using spatial and spectral information and spatial-spectral fusion. Artificial Neural Network models have been gaining prominence among existing classification techniques. They can be applied to data with one, two, or three dimensions. Given the above, this work evaluates Convolutional Neural Network models with one, two, and three dimensions to identify the impact of classifying Hyperspectral Images with different types of convolution. We also expand the comparison to Recurrent Neural Network models, Attention Mechanism, and the Transformer architecture. Furthermore, a novelty pre-processing method is proposed for the classification process to avoid generating data leaks between training, validation, and testing data. The results demonstrated that using 1 Dimension Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and Transformer architectures reduces memory consumption and sample processing time and maintain a satisfactory classification performance up to 99% accuracy on larger datasets. In addition, the Transfomer architecture can approach the 2D-CNN and 3D-CNN architectures in accuracy using only spectral information. The results also show that using two or three dimensions convolution layers improves accuracy at the cost of greater memory consumption and processing time per sample. Furthermore, the pre-processing methodology guarantees the disassociation of training and testing data.N/

    Development of a SQUID magnetometry system for cryogenic neutron electric dipole moment experiment

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    A measurement of the neutron electric dipole moment (nEDM) could hold the key to understanding why the visible universe is the way it is: why matter should predominate over antimatter. As a charge-parity violating (CPV) quantity, an nEDM could provide an insight into new mechanisms that address this baryon asymmetry. The motivation for an improved sensitivity to an nEDM is to find it to be non-zero at a level consistent with certain beyond the Standard Model theories that predict new sources of CPV, or to establish a new limit that constrains them. CryoEDM is an experiment that sought to better the current limit of ∣dn∣<2.9×10−26 e |d_n| < 2.9 \times 10^{-26}\,e\,cm by an order of magnitude. It is designed to measure the nEDM via the Ramsey Method of Separated Oscillatory Fields, in which it is critical that the magnetic field remains stable throughout. A way of accurately tracking the magnetic fields, moreover at a temperature ∌0.5 \sim 0.5\,K, is crucial for CryoEDM, and for future cryogenic projects. This thesis presents work focussing on the development of a 12-SQUID magnetometry system for CryoEDM, that enables the magnetic field to be monitored to a precision of 0.1 0.1\,pT. A major component of its infrastructure is the superconducting capillary shields, which screen the input lines of the SQUIDs from the pick up of spurious magnetic fields that will perturb a SQUID's measurement. These are shown to have a transverse shielding factor of >1×107> 1 \times 10^{7}, which is a few orders of magnitude greater than the calculated requirement. Efforts to characterise the shielding of the SQUID chips themselves are also discussed. The use of Cryoperm for shields reveals a tension between improved SQUID noise and worse neutron statistics. Investigations show that without it, SQUIDs have an elevated noise when cooled in a substantial magnetic field; with it, magnetostatic simulations suggest that it is detrimental to the polarisation of neutrons in transport. The findings suggest that with proper consideration, it is possible to reach a compromise between the two behaviours. Computational work to develop a simulation of SQUID data is detailed, which is based on the Laplace equation for the magnetic scalar potential. These data are ultimately used in the development of a linear regression technique to determine the volume-averaged magnetic field in the neutron cells. This proves highly effective in determining the fields within the 0.1 0.1\,pT requirement under certain conditions

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    DeepMem: ML Models as storage channels and their (mis-)applications

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    Machine learning (ML) models are overparameterized to support generality and avoid overfitting. Prior works have shown that these additional parameters can be used for both malicious (e.g., hiding a model covertly within a trained model) and beneficial purposes (e.g., watermarking a model). In this paper, we propose a novel information theoretic perspective of the problem; we consider the ML model as a storage channel with a capacity that increases with overparameterization. Specifically, we consider a sender that embeds arbitrary information in the model at training time, which can be extracted by a receiver with a black-box access to the deployed model. We derive an upper bound on the capacity of the channel based on the number of available parameters. We then explore black-box write and read primitives that allow the attacker to: (i) store data in an optimized way within the model by augmenting the training data at the transmitter side, and (ii) to read it by querying the model after it is deployed. We also analyze the detectability of the writing primitive and consider a new version of the problem which takes information storage covertness into account. Specifically, to obtain storage covertness, we introduce a new constraint such that the data augmentation used for the write primitives minimizes the distribution shift with the initial (baseline task) distribution. This constraint introduces a level of "interference" with the initial task, thereby limiting the channel's effective capacity. Therefore, we develop optimizations to improve the capacity in this case, including a novel ML-specific substitution based error correction protocol. We believe that the proposed modeling of the problem offers new tools to better understand and mitigate potential vulnerabilities of ML, especially in the context of increasingly large models
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