Hasil untuk "Architecture"

J. Widom

J. Mitola

D. Wetherall, J. Guttag, D. Tennenhouse

R. Allen, D. Garlan

R. Kazman, L. Bass, Mike Webb et al.

Scott C. Burleigh, Adrian Hooke, L. Torgerson et al.

E. Waingold, M. Taylor, D. Srikrishna et al.

J. Magee, J. Kramer

Mark V. Martin, K. Ishii

D. Garlan, Robert T. Monroe, D. Wile

Wouter Termont, Beatriz Esteves

Recent European efforts around digital identity -- the EUDI regulation and its OpenID architecture -- aim high to provide an EU-wide authentication framework. However, its current technical and legislative architecture are based on a limited conceptualization of identity. None of the legal and technical texts involved explicitly define this central term; and their implicit model of the concept does not go beyond a digitalization of identity cards and similar documents. Based on several other standards, we therefore propose a deeper, explicit definition. Grounded in this definition, we identify several issues in the design of OpenID4VCI and OpenID4VP, and show that neither the functional requirements nor the non-functional advantages claimed by OpenID's new trust model surpasses equivalent existing solutions. Also the EUDI legislation itself cannot accommodate its promise of self-sovereign identity. In particular, we criticize the introduction of institutionalized trusted lists, and discuss their economical and political risks. Their potential to decline into an exclusory, recentralized ecosystem endangers the vision of a user-oriented identity management in which individuals are in charge. In anticipation of revisions to the EUDI regulations, we suggest several technical alternatives for the OpenID architecture, as well as paths for future research, addressing a heterogeneity of attestations and providers.

Deepak Vungarala, Md Hasibul Amin, Pietro Mercati et al.

Resistive crossbars enabling analog In-Memory Computing (IMC) have emerged as a promising architecture for Deep Neural Network (DNN) acceleration, offering high memory bandwidth and in-situ computation. However, the manual, knowledge-intensive design process and the lack of high-quality circuit netlists have significantly constrained design space exploration and optimization to behavioral system-level tools. In this work, we introduce LIMCA, a novel fine-tune-free Large Language Model (LLM)-driven framework for automating the design and evaluation of IMC crossbar architectures. Unlike traditional approaches, LIMCA employs a No-Human-In-Loop (NHIL) automated pipeline to generate and validate circuit netlists for SPICE simulations, eliminating manual intervention. LIMCA systematically explores the IMC design space by leveraging a structured dataset and LLM-based performance evaluation. Our experimental results on MNIST classification demonstrate that LIMCA successfully generates crossbar designs achieving $\geq$96% accuracy while maintaining a power consumption $\leq$3W, making this the first work in LLM-assisted IMC design space exploration. Compared to existing frameworks, LIMCA provides an automated, scalable, and hardware-aware solution, reducing design exploration time while ensuring user-constrained performance trade-offs.

Hongyang Shang, An Guo, Shuai Dong et al.

Event-based Cameras (EBCs) are widely utilized in surveillance and autonomous driving applications due to their high speed and low power consumption. Corners are essential low-level features in event-driven computer vision, and novel algorithms utilizing event-based representations, such as Threshold-Ordinal Surface (TOS), have been developed for corner detection. However, the implementation of these algorithms on resource-constrained edge devices is hindered by significant latency, undermining the advantages of EBCs. To address this challenge, a near-memory architecture for efficient TOS updates (NM-TOS) is proposed. This architecture employs a read-write decoupled 8T SRAM cell and optimizes patch update speed through pipelining. Hardware-software co-optimized peripheral circuits and dynamic voltage and frequency scaling (DVFS) enable power and latency reductions. Compared to traditional digital implementations, our architecture reduces latency/energy by 24.7x/1.2x at Vdd = 1.2 V or 1.93x/6.6x at Vdd = 0.6 V based on 65nm CMOS process. Monte Carlo simulations confirm robust circuit operation, demonstrating zero bit error rate at operating voltages above 0.62 V, with only 0.2% at 0.61 V and 2.5% at 0.6 V. Corner detection evaluation using precision-recall area under curve (AUC) metrics reveals minor AUC reductions of 0.027 and 0.015 at 0.6 V for two popular EBC datasets.

Vipin Rathi, Lakshya Chopra, Madhav Agarwal et al.

The telecommunications industry faces a dual transformation: the architectural shift toward Open Radio Access Networks (O-RAN) and the emerging threat from quantum computing. O-RAN disaggregated, multi-vendor architecture creates a larger attack surface vulnerable to crypt-analytically relevant quantum computers(CRQCs) that will break current public key cryptography. The Harvest Now, Decrypt Later (HNDL) attack strategy makes this threat immediate, as adversaries can intercept encrypted data today for future decryption. This paper presents Q-RAN, a comprehensive quantum-resistant security framework for O-RAN networks using NIST-standardized Post-Quantum Cryptography (PQC). We detail the implementation of ML-KEM (FIPS 203) and ML-DSA (FIPS 204), integrated with Quantum Random Number Generators (QRNG) for cryptographic entropy. The solution deploys PQ-IPsec, PQ-DTLS, and PQ-mTLS protocols across all O-RAN interfaces, anchored by a centralized Post-Quantum Certificate Authority (PQ-CA) within the SMO framework. This work provides a complete roadmap for securing disaggregated O-RAN ecosystems against quantum adversaries.

Neethu Kuriakose, Arun Ashok, Christian Grewing et al.

Memristors are promising devices for scalable and low power, in-memory computing to improve the energy efficiency of a rising computational demand. The crossbar array architecture with memristors is used for vector matrix multiplication (VMM) and acts as kernels in neuromorphic computing. The analog conductance control in a memristor is achieved by applying voltage or current through it. A basic 1T1R array is suitable to avoid sneak path issues but suffer from wire resistances, which affects the read and write procedures. A conductance control scheme with a regulated voltage source will improve the architecture and reduce the possible potential divider effects. A change in conductance is also possible with the provision of a regulated current source and measuring the voltage across the memristors. A regulated 2T1R memristor conductance control architecture is proposed in this work, which avoids the potential divider effect and virtual ground scenario in a regular crossbar scheme, as well as conductance control by passing a regulated current through memristors. The sneak path current is not allowed to pass by the provision of ground potential to both terminals of memristors.

Marcelo V. B. da Silva, Maria Barbosa, Anderson Queiroz et al.

Processing computer vision applications (CVA) on mobile devices is challenging due to limited battery life and computing power. While cloud-based remote processing of CVA offers abundant computational resources, it introduces latency issues that can hinder real-time applications. To overcome this problem, computational offloading to edge servers has been adopted by industry and academic research. Furthermore, 5G access can also benefit CVA with lower latency and higher bandwidth than previous cellular generations. As the number of Mobile Operators and Internet Service providers relying on 5G access is growing, it is of paramount importance to elaborate a solution for supporting real time applications with the assistance of the edge computing. Besides that, open-source based platforms for Multi-access Edge Computing (MEC) and 5G core can be deployed to rapid prototyping and testing applications. This paper aims at providing an end-to-end solution of open-source MEC and 5G Core platforms along with a commercial 5G Radio. We first conceived a 5G-edge computing environment to assist near to user processing of computer vision applications. Then a sentiment analysis application is developed and integrated to the proposed 5G-Edge architecture. Finally, we conducted a performance evaluation of the proposed solution and compare it against a remote cloud-based approach in order to highlight the benefits of our proposal. The proposed architecture achieved a 260\% throughput performance increase and reduced response time by 71.3\% compared to the remote-cloud-based offloading.

Amer Tahseen Abu-Jassar, Hani Attar, Ayman Amer et al.

This study investigates the problematic characteristics of contemporary methods for remote and portable patient monitoring. The consideration is based on recent breakthroughs in information technology and progressive strategies for processing and storing biomedical data. The proposed system represents the Medicine 4.0 concept’s next technological leap. Existing methods for remote and portable monitoring of a patient’s status have several vital disadvantages in system flexibility and the convenience of processing and evaluating biomedical data, according to an analysis of these systems. The authors have created a new concept for a Remote Patient Monitoring System (RPMS) that allows for undetectable wear during the patient’s daily activities. Small modules comprising a microcontroller and a collection of medical sensors transfer data in real-time via wireless Internet of Things (IoT) technologies to a cloud service for the attending physician’s processing and visualization convenience. Based on the proposed concept, the authors created a structural diagram of the experimental RPMS and its built prototype. Amazon Web Services (AWS) is used for the real-time processing of biomedical patient data and its subsequent analysis using a graph-based information visualization system. The performed experimental procedure confirmed that the developed experimental RPMS has minimal latency in transmitting data to AWS; it can alert both the patient and the physician about the need for emergency intervention or treatment adjustments, even if critical indicators are detected. Additionally, the proposed system can incorporate components of expert systems and Artificial Intelligence (AI) systems. The authors advocate using the accomplished system for functional diagnostics specialists, paramedics, and cardiologists in medical facilities and the military medical system for rapid diagnosis and direct monitoring of troops’ health state on the battlefield.

Yu. D. Ishkin, N. S. Zakharov, A. V. Rassokhin et al.

In structure of road transport operating costs, expenses for purchasing and storing spare parts can account for up to 45 %. Inventory management systems are most often based on analyzing past demand. A number of factors influence the range and quantity of spare parts needed: vehicle reliability, intensity and conditions of operation, demand, inventory availability at retail networks, and other considerations. At the enterprise level, the most significant criterion for inventory management is the total cost, which includes procurement, delivery and maintenance costs. This study revealed that there is no unified method for determining the optimal stock level of vehicle spare parts. The authors found that spare parts consumption is affected by operational intensity and vehicle reliability, as well as seasonal variations. Analysis of statistical data confirmed the seasonal dependence of spare parts stock levels. This dependence can be described by a harmonic model. It was found that there are some shifts in extreme values depending on the spare part group within a single season. More accurate accounting for these shifts can reduce inventory holding costs. The proposed approach to adjusting spare parts stock level norms in the warehouse will reduce the storage period of spare parts and, consequently, increase the efficiency of inventory management. Based on the studied statistical data, this efficiency is justified by a reduction in inventory valued at 470000 rubles per year. This method of adjusting spare parts stock level norms is applicable to both service and operational enterprises.

Cong Xu, Dimin Niu, N. Muralimanohar et al.

Andrew M. Saxe, Pang Wei Koh, Zhenghao Chen et al.