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Multi-Objective software-Hardware Co-optimization for HD-PIM via Noise-Aware Bayesian optimization 24

Multi-Objective Software-Hardware Co-Optimization for HD-PIM...

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43rd International conference on Computer-Aided Design, ICCAD 2024

作者： Yang, Chien-Yi Zhou, Minxuan Ponzina, Flavio Prakash, Suraj Sathya Ayoub, Raid Mercati, Pietro Subedar, Mahesh Rosing, Tajana University of California San Diego United States Illinois Institute of Technology United States Intel Labs United States

ISBN: (纸本)9798400710773

In hardware accelerator design, software-hardware co-optimization requires intricate trade-offs and tight integration between software algorithms and hardware design to optimize performance, power efficiency, and area (PPA) while ensuring high accuracy. Furthermore, the inherent non-ideality in some emerging hardware technologies poses extra challenges to the co-optimization problem. This paper proposes a novel software-hardware co-optimization framework for hyperdimensional (HD) computing accelerators with emerging ReRAM-based processing in-memory (PIM) technologies, which have shown superior performance and energy efficiency over conventional machine learning accelerators. We first comprehensively characterize the non-trivial trade-offs between design parameters in HD-PIM and PPA and accuracy metrics in HD-PIM. Then, we develop a multi-objective noise-aware Bayesian optimization algorithm to find the Pareto set (optimal trade-offs between metrics) of the HD-PIM design. Our methodology uniquely addresses the stochastic nature of ReRAM by integrating error characteristics into the optimization process, thereby enhancing the quality of the generated designs. Experimental results show that our configurations achieve up to 4.28% accuracy improvement, 35.38% power reduction, 49x timing improvement, and 10% area reduction over a non-optimized design. © 2024 Copyright is held by the owner/author(s).

关键词： Hardware-software codesign

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Local Optimisation Based Detection of Weak Targets in Isolated Forests 24

Local Optimisation Based Detection of Weak Targets in Isolat...

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8th International conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Hu, Jurong Xing, Yanxiao Li, Yuxiao School of Information Science and Engineering Hohai University Jiangsu Nanjing China School of Computer and Software Hohai University Jiangsu Nanjing China

ISBN: (纸本)9798400710094

Aiming at the problem that the iForest algorithm is not sensitive enough to local anomalies and produces a large number of false alarms in the detection results on some low sea state datasets, this paper proposes the iForest algorithm based on an improved splitting strategy. The algorithm introduces the Gini impurity, incorporates the Gini impurity parameter into the splitting strategy, and is more targeted in the selection of splitting attributes and splitting points, thus better adapting to the problem of detecting weak targets under different sea states, and has an excellent performance of high accuracy, low time duration, and low false alarm rate compared with the traditional methods in anomaly detection. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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Beam Pattern optimization Using Pareto-Enhanced Particle Swarm with Cross-Variance optimization (PPS-CVO) Algorithm 4

Beam Pattern Optimization Using Pareto-Enhanced Particle Swa...

引用

4th International Computational Imaging conference, CITA 2024

作者： Fang, Lei Wang, Xiaobin Fang, Sian Sun, Yizhou Liang, Pan Zhang, Feifei Shan, Tao Wang, Sai Zhang, Meng Heifei iFlytek Digital Technology Co. Ltd. Hefei230088 China

ISBN: (纸本)9781510688834

In this paper, a novel metaheuristic optimization algorithm, the Pareto-Enhanced Particle Swarm with Cross-Variance optimization (PPS-CVO) Algorithm, is proposed for the task of underwater acoustic beam pattern optimization. The proposed method introduces several ideas: (1) the incorporation of a Crossover Variational Operator, which enhances population diversity and improves the algorithm's ability to escape local optima;(2) using the vector difference between two randomly selected high-performing feasible solutions as the step size for particle perturbation. In the early iterations, the step size corresponding to the vector difference is relatively large, allowing feasible solutions to search across the entire solution space, while in the later iterations, the step size becomes smaller, enabling more refined local searches;(3) applying the Pareto distribution to adjust the step size during perturbation, leveraging its long-tail characteristics to enhance the algorithm's ability to escape local optima during local search in the stages of iteration;and (4) a two-phase approach that balances exploration and exploitation throughout the optimization process. During each iteration, the exploration phase allows the feasible solutions to perform large step-size searches, and in the second phase, small step-size perturbations are introduced for local search to prevent the exploration from missing the optimal solution. The performance of the PPS-CVO algorithm was validated through numerical simulations using the CEC 2015 and CEC 2017 benchmark suites, which include a total of 20 test functions (10 from CEC 2015 and 10 from CEC 2017), as well as beamforming optimization tasks. Results from these benchmark suites demonstrate that the PPS-CVO algorithm outperforms 10 well-known algorithms in most unimodal and multimodal optimization problems. Specifically, in the underwater acoustic beamforming optimization problem, which is a high-dimensional, nonlinear challenge involving th

关键词： Underwater acoustics

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Leveraging Artificial Intelligence to Revolutionize Rigless ESP Planning and Intervention optimization

Leveraging Artificial Intelligence to Revolutionize Rigless ...

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2025 International Petroleum Technology conference, IPTC 2025

作者： Zhao, Mengpan Li, Qijun Hong, Yuandong Zhang, Li Xue, Fan Dai, Rui NOVA Rigless Technology Co. Ltd. Sichuan Chengdu China

ISBN: (纸本)9781959025436

The deployment of Electric Submersible Pumps (ESPs) is a critical process in artificial lift for oil and gas wells, traditionally requiring rig-based operations that are both costly and time-consuming. Rigless ESP interventions offer a promising alternative, providing substantial cost and safety advantages. However, selecting the appropriate system has historically been a complex and uncertain process, hindering broader application. This paper highlights the advantages of Rigless ESP replacements over traditional rig-based methods, describe the applicability and limitations of different types of Rigless ESP technology and addresses the challenges associated with selecting the appropriate system. It also introduces the Rigless ESP Advisor, an innovative AI-powered software designed to streamline the planning and optimization of Rigless ESP interventions. The Rigless ESP Advisor combines analytical formulas and machine learning algorithms to analyze well data and recommend the optimal Rigless ESP system. Engineers input key downhole parameters and production forecasts into the software, which then rigorously analyzes the data to provide precise, evidence-based recommendations. The Advisor's machine learning model, trained on historical well performance data, ensures that each well receives the most suitable solution. A case study was conducted on a high-producing well that recently experienced pump failure. The Rigless ESP Advisor recommended a 5.5'' Rigless WDESP (Wireline Deployed ESP) system based on the well's parameters and a 30-day production forecast. The recommended system was successfully implemented, with on-site operations confirming its reliable installation and retrieval. Copyright 2025, International Petroleum Technology conference.

关键词： Submersible pumps

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10th Russian Supercomputing Days conference, RuSCDays 2024

10th Russian Supercomputing Days Conference, RuSCDays 2024

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10th Russian Supercomputing Days conference, RuSCDays 2024

ISBN: (纸本)9783031784613

The proceedings contain 43 papers. The special focus in this conference is on Russian Supercomputing Days. The topics include: Comparison the Decomposition and Partitioning Approaches of Large Number of Boundary-Conforming Grids Covered Fractured Geological Media;Docking and Post-Processing of 1 Million Molecules from the CNCL Database in Search of SARS-CoV-2 Mpro Inhibitors;domain Decomposition for the Numerical Solution of the Cahn-Hilliard Equation;Efficient Parallel Computing for Dynamic Free Surface Flows: A Study with FLOW-3D;microwave Tomography Method for Determining Inhomogeneities in the Inverse Diffraction Problem;numerical Simulation of the Laser Pulse Propagation in Thin Cloud Layers;on the Problems of Convergence of Iterative Methods for Solving Two-Coefficient Inverse Problems of Ultrasound Tomography;parallel algorithms for Calculating Problems of Supersonic Cold Gas-Dynamic Spraying Nanoparticles on Substrates;parallel algorithms for Solving Mass Transfer Equations in the "Fracture Set – Matrix System";parallel Efficiency Analysis of Reactive Transport Simulations Using the GeRa software;performance of Parallel NetCDF Output in the INM RAS Earth System Model;quantum-Chemical Calculations of the Enthalpy of Formation of Isomeric 5/6/5 Tricyclic Tetrazolotetrazine Derivatives Annelated with Nitroazoles;simulating the Black Sea 7Be Transport with Nested General Circulation Models;stresses in Thin Optical Films: Results of high-performance Atomistic Simulation;the Influence of the Kelvin-Helmholtz Instability on the Shape and Decay of Molecular Clouds Remnants Moving Behind the Shock Wave After a Supernova Explosion;the Numerical Dispersion Mitigation in Three-Dimensional Wavefields;towards an Adaptation of the nonlinear Harmonics Method Realized in an Unstructured Flow Solver for Simulation of Turbomachinery Problems on Supercomputers;Using the MULTICOMP Package to Predict the Properties of Polymer-Based Materials;a Study of a Composable Approach to Pa

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10th Russian Supercomputing Days conference, RuSCDays 2024

10th Russian Supercomputing Days Conference, RuSCDays 2024

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10th Russian Supercomputing Days conference, RuSCDays 2024

ISBN: (纸本)9783031784583

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A Comparison of Formulations for Aircraft Deconfliction 4th

A Comparison of Formulations for Aircraft Deconfliction

引用

4th International conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Spencer Trindade, Renan D’Ambrosio, Claudia LIX CNRS Ecole Polytechnique Institut Polytechnique de Paris 1 rue Honoré d’Estienne d’Orves Bâtiment Alan Turing Palaiseau91120 France

ISBN: (纸本)9783031812408

In this work, we aim to compare different formulations and off-the-shelf solvers to solve a problem in air traffic management to global optimality, or a lower and an upper bound to it when it is not possible. In particular, we focus on the aircraft deconfliction problem, where we are given n aircraft, their position at time 0, and their (straight) trajectories. We wish to identify and solve potential pairwise conflict by temporarily modifying the aircraft’s trajectory. A pair of aircraft is in conflict when they do not respect a minimum, predefined safety distance. In general, conflicts could be solved both varying the aircraft’s speed or trajectory, but in this paper we only consider the latter, more precisely heading-angle deviations. The problem has been formulated as a mixed integer nonlinear program (MINLP). We compare this formulation, solved by open-source MINLP solvers for global optimization, against a reformulation that shows a larger number of variables and constraints but only separable nonconvexities. We solve such a separable formulation with the same MINLP solvers or the Sequential Convex Mixed Integer nonlinear Programming method. The separable formulation, despite being larger, facilitates some solvers in finding good-quality solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Global optimization

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Combined First- and Second-Order Directions for Deep Neural Networks Training 4th

Combined First- and Second-Order Directions for Deep Neura...

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4th International conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Martínez, Ángeles Viola, Marco Yousefi, Mahsa University of Trieste Trieste Italy Dublin City University Dublin Ireland

ISBN: (纸本)9783031812408

In this work, we consider a novel stochastic optimization algorithm to solve the unconstrained, nonlinear, and non-convex optimization problems arising in the training of deep neural networks. The new algorithm is based on the combination of first- and second-order information, namely, at each step the computed search direction linearly combines a variance-reduced gradient and a stochastic limited memory quasi-Newton direction. We report computational experiments showing the performance of the proposed optimizer in the training of a modern deep residual neural network for image classification tasks. The numerical results show that the proposed algorithm exhibits comparable or superior performance than the state-of-the-art Adam optimizer, without the agonizing pain of tuning its many hyperparameters. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convex optimization

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Research on Solving Mathematical Programming Problems Based on Intelligent optimization algorithms

Research on Solving Mathematical Programming Problems Based ...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International conference on

作者： Yan Wang Wuhan Technology and Business University Wuhan China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper studies the solution methods of mathematical programming problems based on intelligent optimization algorithms, focusing on the application and performance of GA and PSO in complex mathematical programming. First, a hybrid intelligent algorithm that integrates the characteristics of genetic algorithm and particle swarm optimization is designed. By optimizing the coding structure, selection strategy and crossover mutation operator, the global search ability and convergence speed of the algorithm are improved. Secondly, a mathematical model suitable for integer programming and nonlinear programming is constructed, and experimental verification of problems of different scales is carried out in combination with the simulation platform. Experiments show that in typical test functions and actual application scenarios, the improved algorithm improves the solution accuracy by 15.3% and shortens the convergence time by 18.7 % compared with the traditional method. The simulation results prove the effectiveness and robustness of the improved algorithm through quantitative data analysis, especially in solving high-dimensional nonlinear problems. The research in this paper not only expands the application of intelligent optimization algorithms in the field of mathematical programming, but also provides a theoretical basis and practical reference for solving actual engineering optimization problems.

关键词： Power engineering Accuracy Simulation Programming Robustness Particle swarm optimization optimization Mathematical programming Genetic algorithms Convergence

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Genetic Algorithm optimization based Control for Heat Integrated Distillation Column

Genetic Algorithm Optimization based Control for Heat Integr...

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Learning and Technology conference (L&T)

作者： Nura Musa Tahir Jie Zhang Matthew Armstrong Electrical Engineering Newcastle University Newcastle Upon Tyne UK Chemical Engineering Newcastle University Newcastle Upon Tyne UK

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Controlling nonlinear heat integrated distillation column (HIDiC) systems is notably challenging due to their complex dynamics and inherent nonlinearities. This study examines the effectiveness of genetic algorithm (GA) optimization in enhancing proportional-integral (PI) controller performance for HIDiC systems. A nonlinear mechanistic model of a HIDiC was adopted, with the GA applied to tune the PI controllers for optimal control performance. Control performance was tested under varying setpoints and feed composition disturbances. Simulation results demonstrate that the GA-tuned PI controller provides robust control performance, maintaining stability and high product purity even with significant disturbances. Initial oscillations due to feed composition disturbance are minimal and quickly settle down, confirming the controller's capability to achieve precise control and high separation performance.

关键词： Heating systems Robust control PI control Distillation equipment Simulation Stability analysis Feeds optimization Tuning Genetic algorithms

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