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Error Mitigation Using Optimized Redundancy for Composite algorithms in FPGAs

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第2期60卷 2143-2152页

作者： Garcia-Astudillo, Luis A. Lindoso, Almudena Entrena, Luis Univ Carlos III Madrid Dept Elect Technol E-28911 Leganes Spain

Error mitigation techniques have become mandatory in aerospace applications to cope with soft errors. Approximate error mitigation techniques are an attractive solution to reduce the overheads caused by conventional approaches, such as triple modular redundancy. These techniques use approximate redundant copies of the target design, which can be implemented with less resources, at the expense of slightly reducing the precision of the result in case of error. In this work, we propose optimized redundancy for composite algorithms, a novel hardening technique for algorithms that may be decomposed into more simple parts. Instead of adding identical redundant modules, we use complementary modules that can be composed to implement the target design. These complementary modules can also be compared to detect errors. However, when they are correct, they produce an exact result instead of an approximate result. The experimental results show that this technique can reduce the overhead and provide a better tradeoff between overhead and precision than existing approximate techniques.

关键词： Redundancy approximation algorithms Field programmable gate arrays Error correction Radiation hardening (electronics) Aerospace electronics Single event upsets

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Quantum Optimization Methods for Satellite Mission Planning

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IEEE ACCESS 2024年 12卷 71808-71820页

作者： Makarov, Anton Perez-Herradon, Carlos Franceschetto, Giacomo Taddei, Marcio M. Osaba, Eneko del Barrio Cabello, Paloma Villar-Rodriguez, Esther Oregi, Izaskun GMV Tres Cantos 28760 Madrid Spain IFF CSIC Inst Fundamental Phys Madrid 28006 Spain Barcelona Inst Sci & Technol Inst Ciencies Foton ICFO Castelldefels 08860 Barcelona Spain Basque Res & Technol Alliance BRTA TECNALIA Derio 48160 Spain European Univ Gasteiz EUNEIZ Dept New Interact Technol Vitoria 01013 Spain

Satellite mission planning for Earth observation satellites is a combinatorial optimization problem that consists of selecting the optimal subset of imaging requests, subject to constraints, to be fulfilled during an orbit pass of a satellite. The ever-growing amount of satellites in orbit underscores the need to operate them efficiently, which requires solving many instances of the problem in short periods of time. However, current classical algorithms often fail to find the global optimum or take too long to execute. Here, we approach the problem from a quantum computing point of view, which offers a promising alternative that could lead to significant improvements in solution quality or execution speed in the future. To this end, we study a planning problem with a variety of intricate constraints and discuss methods to encode them for quantum computers. Additionally, we experimentally assess the performance of quantum annealing and the quantum approximate optimization algorithm on a realistic and diverse dataset. Our results identify key aspects like graph connectivity and constraint structure that influence the performance of the methods. We explore the limits of today's quantum algorithms and hardware, providing bounds on the problems that can be currently solved successfully and showing how the solution degrades as the complexity grows. This work aims to serve as a baseline for further research in the field and establish realistic expectations on current quantum optimization capabilities.

关键词： Satellites Planning Optimization methods Cameras Quantum annealing approximation algorithms Annealing Combinatorial optimization earth observation quantum annealing quantum approximate optimization algorithm quantum computing satellite mission planning

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A 1.9999-approximation Algorithm for Vertex Cover on String Graphs 40

A 1.9999-Approximation Algorithm for Vertex Cover on String ...

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40th International Symposium on Computational Geometry, SoCG 2024

作者： Lokshtanov, Daniel Panolan, Fahad Saurabh, Saket Xue, Jie Zehavi, Meirav University of California Santa BarbaraCA United States University of Leeds United Kingdom The Institute of Mathematical Sciences HBNI Chennai India New York University Shanghai China Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9783959773164

Vertex Cover is a fundamental optimization problem, and is among Karp’s 21 NP-complete problems. The problem aims to compute, for a given graph G, a minimum-size set S of vertices of G such that G−S contains no edge. Vertex Cover admits a simple polynomial-time 2-approximation algorithm, which is the best approximation ratio one can achieve in polynomial time, assuming the Unique Game Conjecture. However, on many restrictive graph classes, it is possible to obtain better-than-2 approximation in polynomial time (or even PTASes) for Vertex Cover. In the club of geometric intersection graphs, examples of such graph classes include unit-disk graphs, disk graphs, pseudo-disk graphs, rectangle graphs, etc. In this paper, we study Vertex Cover on the broadest class of geometric intersection graphs in the plane, known as string graphs, which are intersection graphs of any connected geometric objects in the plane. Our main result is a polynomial-time 1.9999-approximation algorithm for Vertex Cover on string graphs, breaking the natural 2 barrier. Prior to this work, no better-than-2 approximation (in polynomial time) was known even for special cases of string graphs, such as intersection graphs of segments. Our algorithm is simple, robust (in the sense that it does not require the geometric realization of the input string graph to be given), and also works for the weighted version of Vertex Cover. Due to a connection between approximation for Independent Set and approximation for Vertex Cover observed by Har-Peled, our result can be viewed as a first step towards obtaining constant-approximation algorithms for Independent Set on string graphs. © Daniel Lokshtanov, Fahad Panolan, Saket Saurabh, Jie Xue, and Meirav Zehavi.

关键词： approximation algorithms

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A fast (2+2/7)-approximation algorithm for capacitated cycle covering

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MATHEMATICAL PROGRAMMING 2022年第1-2期192卷 497-518页

作者： Traub, Vera Trobst, Thorben Swiss Fed Inst Technol Zurich Switzerland Univ Calif Irvine Irvine CA 92697 USA

We consider the capacitated cycle covering problem: given an undirected, complete graph G with metric edge lengths and demands on the vertices, we want to cover the vertices with vertex-disjoint cycles, each serving a demand of at most one. The objective is to minimize a linear combination of the total length and the number of cycles. This problem is closely related to the capacitated vehicle routing problem (CVRP) and other cycle cover problems such as min-max cycle cover and bounded cycle cover. We show that a greedy algorithm followed by a post-processing step yields a (2 + 2/7)-approximation for this problem by comparing the solution to a polymatroid relaxation. We also show that the analysis of our algorithm is tight and provide a 2+ epsilon lower bound for the relaxation.

关键词： approximation algorithms

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Inverse Decoupling Fuzzy Active Disturbance Rejection Control for Supercritical CO2 Extraction

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IEEE ACCESS 2024年 12卷 36466-36478页

作者： Cao, Menglong Zhu, Zhaosen Ju, Yunpeng Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266100 Peoples R China

Aiming at the problem of strong coupling and time-delay issues in the temperature-pressure control of supercritical CO2 extraction systems, an inverse decoupling fuzzy active disturbance rejection control method based on pole approximation is proposed. Firstly, by approximating the time-delay link of the controlled subsystem using pole approximation, the matching degree of the two-input signals of the linear extended state observer (LESO) is improved. Secondly, an inverse decoupling time-synchronized active disturbance rejection control method is proposed to address the coupling link of temperature-pressure as well as the complex control environments and numerous disturbances. Furthermore, the controller parameters are tuned using bandwidth method and fuzzy control rules, and theoretical analysis of this method is conducted. In addition, comparative simulations are conducted to validate this method against other approaches. The results indicate that this method exhibits better tracking performance, disturbance rejection capability, and robustness in the temperature-pressure control of supercritical CO2 extraction systems. Finally, the feasibility of this method in reality was verified through experimental platforms.

关键词： Couplings Process control Mathematical models Control systems Thermal stability Observers Valves Temperature control Pressure control Fuzzy systems Carbon dioxide approximation algorithms CO2 extraction active disturbance rejection control inverse decoupling pole approximation fuzzy control

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Structural iterative rounding for generalized k-median problems

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MATHEMATICAL PROGRAMMING 2024年 1-54页

作者： Gupta, Anupam Moseley, Benjamin Zhou, Rudy NYU Comp Sci Dept 251 Mercer St New York NY 10012 USA Carnegie Mellon Univ Tepper Sch Business 5000 Forbes Ave Pittsburgh PA 15213 USA

This paper considers approximation algorithms for generalized k-median problems. This class of problems can be informally described as k-median with a constant number of extra constraints, and includes k-median with outliers, and knapsack median. Our first contribution is a pseudo-approximation algorithm for generalized k-median that outputs a 6.387-approximate solution, with a constant number of fractional variables. The algorithm builds on the iterative rounding framework introduced by Krishnaswamy, Li, and Sandeep for k-median with outliers. The main technical innovation is allowing richer constraint sets in the iterative rounding and taking advantage of the structure of the resulting extreme points. Using our pseudo-approximation algorithm, we give improved approximation algorithms for k-median with outliers and knapsack median. This involves combining our pseudo-approximation with pre- and post-processing steps to round a constant number of fractional variables at a small increase in cost. Our algorithms achieve approximation ratios 6.994+& varepsilon;and 6.387+& varepsilon;for k-median with outliers and knapsack median, respectively. These improve on the best-known approximation ratio 7.081+& varepsilon;for both problems as reported (Krishnaswamy et al. in: Proceedings of the 50th Annual ACMSIGACT Symposium on Theory of Computing, 2018).

关键词： approximation algorithms Clustering Facility location Linear program rounding

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A Compressed Spiking Neural Network Onto a Memcapacitive In-Memory Computing Array

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IEEE MICRO 2024年第1期44卷 8-16页

作者： Oshio, Reon Sugahara, Takuya Sawada, Atsushi Kimura, Mutsumi Zhang, Renyuan Nakashima, Yasuhiko Nara Inst Sci & Technol Grad Sch Sci & Technol Ikoma 6300192 Japan Ryukoku Univ Grad Sch Sci & Technol Otsu 5202194 Japan

Spiking neural networks (SNNs) enable the execution of deep learning-compatible tasks and approximation algorithms with low latency and low power consumption by operating on a neuromorphic system. Adopting analog in-memory computing (AiMC) in a neuromorphic system can build a system that has an advantage in memory density over a pure digital implementation. However, sensing the AiMC output with simple circuitry inevitably leads to unintended nonlinearities. In this study, we design a neuromorphic circuit using memcapacitive AiMC synapses with ultra-low power. We combine circuit nonlinearity-aware training (CNAT) with network compression techniques to prevent the SNN from losing accuracy caused by the neuron circuit's nonlinearity and the synapse's low resolution. The training runs on a machine learning framework and does not need to incorporate computationally intensive SPICE simulations. As simulated, our circuit performs MNIST classifications with almost no loss from ideal accuracy (97.64%) and consumes 15.7 nJ per inference.

关键词： Training Neuromorphics Quantization (signal) Integrated circuit modeling Neurons Membrane potentials Mathematical models Deep learning approximation algorithms SPICE Machine learning

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Enhancing Knapsack-Based Financial Portfolio Optimization Using Quantum Approximate Optimization Algorithm

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IEEE ACCESS 2024年 12卷 183779-183791页

作者： Huot, Chansreynich Kea, Kimleang Kim, Tae-Kyung Han, Youngsun Pukyong Natl Univ Dept AI Convergence Busan 48513 South Korea Chungbuk Natl Univ Dept Management Informat Syst Cheongju 28644 Chungcheongbuk South Korea

Portfolio optimization is a primary component of the decision-making process in finance, aiming to tactfully allocate assets to achieve optimal returns while considering various constraints. Herein, we proposed a method that uses the knapsack-based portfolio optimization problem and incorporates the quantum computing capabilities of the quantum walk mixer with the quantum approximate optimization algorithm (QAOA) to address the challenges presented by the NP-hard problem. Additionally, we present the sequential procedure of our suggested approach and demonstrate empirical proof to illustrate the effectiveness of the proposed method in finding the optimal asset allocations across various constraints and asset choices. Moreover, we discuss the effectiveness of the QAOA components in relation to our proposed method. Consequently, our study successfully achieves the approximate ratio of the portfolio optimization technique using a circuit layer of $p\geqslant 3$ , compared to the classical best-known solution of the knapsack problem. Our proposed methods potentially contribute to the growing field of quantum finance by offering insights into the potential benefits of employing quantum algorithms for complex optimization tasks in financial portfolio management.

关键词： Portfolios Optimization Quantum computing Computational modeling approximation algorithms Security Standards Resource management Linear programming Investment Best-known solution (BKS) portfolio optimization knapsack problem quantum approximate optimization algorithm (QAOA)

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Exploiting Stragglers in Distributed Computing Systems With Task Grouping

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第6期17卷 3613-3626页

作者： Adikari, Tharindu Al-Lawati, Haider Lam, Jason Hu, Zhenhua Draper, Stark C. Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Oman Data Pk Muscat Oman Huawei Technol Markham L3R 5A4 ON Canada

We consider the problem of stragglers in distributed computing systems. Stragglers, which are compute nodes that unpredictably slow down, often increase the completion times of tasks. One common approach to mitigating stragglers is work replication, where only the first completion among replicated tasks is accepted, discarding the others. However, discarding work leads to resource wastage. In this article, we propose a method for exploiting the work completed by stragglers rather than discarding it. The idea is to increase the granularity of the assigned work, and to increase the frequency of worker updates. We show that the proposed method reduces the completion time of tasks via experiments performed on a simulated cluster as well as on Amazon EC2 with Apache Hadoop.

关键词： Clustering algorithms Standards Prevention and mitigation Optimization Hardware Stochastic processes Online services Encyclopedias approximation algorithms Proposals Distributed systems stragglers task scheduling

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Energy Efficiency Optimization for URLLC in RIS-Aided User-Centric Networks

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IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 110-114页

作者： Luo, Tao Lu, Hancheng Chong, Baolin Univ Sci & Technol China CAS Key Lab Wireless Opt Commun Hefei 230027 Peoples R China

User-centric networking is expected to be a promising technology to implement ultra-reliable and low-latency communication (URLLC). However, the densely deployed access points (APs) in a user-centric network (UCN) will involve significant hardware costs and energy consumption. To address this issue, we propose a reconfigurable intelligent surface (RIS)-aided UCN for URLLC. Then, a joint optimization problem with consideration of active beamforming at APs and passive beamforming at RISs is formulated to achieve optimal energy efficiency (EE). Since the problem is intractable and non-convex, we develop an alternating optimization algorithm based on the inner approximation framework to solve it efficiently. Numerical results verify that the proposed algorithm outperforms baseline algorithms regarding EE. Particularly, with the proposed algorithm, our RIS-aided UCN achieves up to 147% performance gains in terms of EE compared to traditional UCN.

关键词： Ultra reliable low latency communication Array signal processing Optimization Power demand approximation algorithms Reconfigurable intelligent surfaces Hardware Central Processing Unit Quality of service Interference Reconfigurable intelligent surface user-centric network ultra-reliable and low-latency communication finite blocklength transmission energy efficiency

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