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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Huifeng Ke Sihao Liu Licheng Guo Zifan He Linghao Song Suhail Basalama Yuze Chi Tony Nowatzki Jason Cong University of California Los Angeles RapidStream Design Automation Inc.

ISBN: (数字)9798331502812

ISBN: (纸本)9798331502829

In FPGAs, high communication latency in multi-die chips has driven the integration of hardened networks-on-chip (NoCs) in commercial devices. However, for programming FPGAs with high-level synthesis (HLS), existing tools only provide low-level cumbersome abstractions, and only work for offloading memory accesses. Furthermore, these abstractions remain inaccessible to programmers due to their reliance on placement knowledge. While automatically leveraging the NoC without manual intervention is ideal, it poses several challenges: 1. Managing the trade-off in resource utilization between the hard NoC and the Programmable Logic (PL). 2. Allocating limited hard NoC resources between different communication in the designs. 3. Aligning hard NoC and PL placement even though the actual PL placement cannot be determined beforehand. We address these challenges by developing NoH, the first HLS flow that automates hard NoC offloading. First, we develop a formal NoC-aware placement algorithm that leverages integer linear programming (ILP) and considers the first two challenges for offloading external memory accesses and latency-insensitive communication between modules. Then, we arrange the ports synergistically with PL modules via a port-affinity model that approximates the PL placement. Finally, NoH is integrated into an end-to-end HLS flow and evaluated on 4 workloads with diverse communication patterns. NoH gains 20% FPGA frequency over AMD tools by leveraging the hard NoC. Compared to AutoBridge [1], a recent high-level physical synthesis technique that optimizes frequency but does not consider the hard NoC, NoH never fails place-and-route by offloading inter-die crossings (AutoBridge fails in 31% of workload configurations tested) and is faster (6%) for the rest.

关键词： Frequency synthesizers Computational modeling Manuals Integer linear programming approximation algorithms Resource management Logic Field programmable gate arrays

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Stochastic Optimal Control for Nonlinear Systems based on Sampling & Deep Learning

Stochastic Optimal Control for Nonlinear Systems based on Sa...

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IEEE Conference on Decision and Control

作者： Andreas Bott Kirill Kuroptev Florian Steinke Department of Electrical Engineering Technical University of Darmstadt Darmstadt Germany

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Stochastic optimal control requires an adequate representation of state uncertainty. For stochastic nonlinear systems, the probability distribution over the states given measurements can often not be represented in closed form. In this paper, we thus propose to address this control task based on Monte-Carlo sampling, integrating the state estimation step with stochastic gradient descent-based control optimisation. A deep neural network approximation of the nonlinear system is the key to speeding up both parts. We motivate and demonstrate the approach for district heating systems, where the security of supply shall be guaranteed with high probability in the face of uncertain consumer demands. Our conceptually simple approach enables representing multimodal distributions and achieving computation times feasible for the online operation of district heating systems.

关键词： Monte Carlo methods Heuristic algorithms District heating Stochastic processes Optimal control Artificial neural networks approximation algorithms Nonlinear systems State estimation Optimization

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A Simplified 256-APSK Soft Demapping Method and Implementation

A Simplified 256-APSK Soft Demapping Method and Implementati...

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Intelligent Communications and Computing (ICC), International Conference on

作者： Wenxi Liu Zhongqiu He Qingling Liu School of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331530365

ISBN: (纸本)9798331530372

High-order amplitude phase shift keying (APSK) presents significant challenges in both hardware and software implementations for demapping due to its characteristics such as a large number of constellation points, high computational complexity, and pronounced nonlinear property. In this paper, a low complexity soft demapping method is proposed, where APSK modulation is equivalently decomposed into independent PSK and ASK modulations, and then the computational process of the Max-Log-Map algorithm is simplified in accordance with their respective distributions, resulting in an efficient yet low complexi-ty demapping algorithm. Simulation results indicate that for 256-APSK, the proposed method achieves identical performance to traditional demapping algorithms, with significantly reduced computational complexity.

关键词： Phase shift keying Simulation Amplitude shift keying Modulation approximation algorithms Software Hardware Computational efficiency Computational complexity Standards

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Model-based Policy Optimization using Symbolic World Model

Model-based Policy Optimization using Symbolic World Model

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Andrey Gorodetskiy Konstantin Mironov Aleksandr Panov Center of Cognitive Modeling Moscow Institute of Physics and Technology Dolgoprudny Russia Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Artificial Intelligence Research Institute Moscow Russia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The application of learning-based control methods in robotics presents significant challenges. One is that model-free reinforcement learning algorithms use observation data with low sample efficiency. To address this challenge, a prevalent approach is model-based reinforcement learning, which involves employing an environment dynamics model. We suggest approximating transition dynamics with symbolic expressions, which are generated via symbolic regression. approximation of a mechanical system with a symbolic model has fewer parameters than approximation with neural networks, which can potentially lead to higher accuracy and quality of extrapolation. We use a symbolic dynamics model to generate trajectories in model-based policy optimization to improve the sample efficiency of the learning algorithm. We evaluate our approach across various tasks within simulated environments. Our method demonstrates superior sample efficiency in these tasks compared to model-free and model-based baseline methods.

关键词： Heuristic algorithms Robot kinematics Neural networks Reinforcement learning Predictive models Transformers approximation algorithms Inference algorithms Trajectory Optimization

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Research on Optimization of Multi-Beam Line Layout Based on a Novel Hybrid Algorithm for Solving Multi-objective Optimization Problems

Research on Optimization of Multi-Beam Line Layout Based on ...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Minfei Dai Guangbei Zhang Meng Meng Nantong University Nantong China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

CBQNA is an effective hybrid algorithm proposed for solving multi-objective optimization engineering problems with inequality constraints. The weighted sum technique and BFGS quasi-Newton’s method are combined to determine a descent search direction for solving multi-objective optimization problems. A cautious BFGS iterative format is utilized to approximate the Hessian matrices of the objective functions in order to improve the computational efficiency and maintain rapid convergence. Multi-beam sounding technology has been widely used in seabed landform exploration. It is of great significance to study its working principle and establish a mathematical model for optimizing multi-beam surveying line. In this paper, considering the characteristics of sea area, measurement efficiency and economic objectives, the best multibeam line measurement model is designed based on space geometry derivation and CBQNA optimization of double objective programming. This scheme can not only effectively cover most of the sea area, but also ensure the accuracy and integrity of the measurement data.

关键词： Surveys Accuracy Area measurement Sea measurements Programming Linear programming approximation algorithms Mathematical models Optimization Convergence

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Adaptive Optimal Output-Feedback Control of Discrete-Time Systems based on Hybrid Iteration

Adaptive Optimal Output-Feedback Control of Discrete-Time Sy...

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IEEE Conference on Decision and Control

作者： Sitong Liu Weinan Gao Zhong-Ping Jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Lianning China Department of Electrical and Computer Engineering Tandon School of Engineering New York University Brooklyn NY USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, a novel adaptive dynamic programming (ADP) algorithm, named output-feedback hybrid iteration (HI),is proposed to address the adaptive optimal control problem of discrete-time linear systems. The proposed output-feedback HI strategy learns the optimal control policy through two phases. First, a novel data-driven value-iteration (VI) scheme is employed to learn an admissible output-feedback control policy using the input/output data without relying on the knowledge of system matrices. Then, with the obtained admissible control policy, the optimal output feedback-control policy is approximated with an accelerated convergence rate through output-feedback policy iteration (PI). Online input/output data are utilized to reconstruct the full state of the system and integrated into the new output-feedback HI algorithm. Simulation results are presented and demonstrate the efficacy and practicality of the proposed output-feedback HI approach in comparison with traditional PI and VI techniques.

关键词： Linear systems Discrete-time systems Adaptive systems Simulation Heuristic algorithms Optimal control approximation algorithms Dynamic programming Convergence

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The Submodular Santa Claus Problem

arXiv

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arXiv 2024年

作者： Bamas, Étienne Morell, Sarah Rohwedder, Lars ETH AI Center Switzerland TU Berlin Germany Maastricht University Netherlands

We consider the problem of allocating indivisible resources to players so as to maximize the minimum total value any player receives. This problem is sometimes dubbed the Santa Claus problem and its different variants have been subject to extensive research towards approximation algorithms over the past two decades. In the case where each player has a potentially different additive valuation function, Chakrabarty, Chuzhoy, and Khanna [FOCS’09] gave an O(nΕ)-approximation algorithm with polynomial running time for any constant Ε > 0 and a polylogarithmic approximation algorithm in quasi-polynomial time. We show that the same can be achieved for monotone submodular valuation functions, improving over the previously best algorithm due to Goemans, Harvey, Iwata, and Mirrokni [SODA’09], which has an approximation ratio of more than √n. Our result builds up on a sophisticated LP relaxation, which has a recursive block structure that allows us to solve it despite having exponentially many variables and constraints. © 2024, CC0.

关键词： approximation algorithms

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DRL-Based Joint UAV Trajectory, Nest Scheduling and Resource Optimization for Multi-UAV Aided Power Inspection System

DRL-Based Joint UAV Trajectory, Nest Scheduling and Resource...

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International Conference on Computer and Communications (ICCC)

作者： Xuan Luo Yunfei Gao Rongtao Liao Bingyao Hu Ronghao Yang Information & Communication Branch State Grid Hubei Electric Power Company Wuhan China School of Electronic Information Wuhan University Wuhan China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Unmanned aerial vehicle (UAV)-aided inspection system is considered a promising technology due to the high mobility and real-time monitoring capabilities. In this paper, we consider a multi-UAV aided power inspection system with no-fly zones (NFZs), where a set of UAVs complete capturing images/video tasks near the inspection points. In particular, the mobile nests are scheduled to recharge the UAV when its battery falls below a certain threshold. The goal of this paper is to minimize the total inspection time of the system by jointly optimizing the multi-UAV inspection tasks, UAV trajectories, and mobile nest scheduling. On the one hand, as the number of inspection points $M$ increases, the algorithm's complexity grows factorially, i.e., $\mathcal{O}(M-1)!$ . In addition, the formulated problem is a multivariable coupled non-convex problem, making it difficult to solve using traditional methods such as successive convex approximation or interior-point methods. To address these challenges, we first use the Kmeans++ algorithm to allocate the total tasks to each UAV, reducing the complexity of subsequent trajectory optimization. Then, for each UAV, we design its inspection trajectory based on an modified multi-step dueling double deep Q learning network (MSD-DDQN) algorithm and schedule the mobile nests for UAV recharging utilizing the A* algorithm. Finally, simulations validate the effectiveness of the proposed method. Compared to the baselines, the proposed approach accelerates the algorithm's convergence speed and reduces task completion time by nearly 37%.

关键词： Schedules Inspection Autonomous aerial vehicles approximation algorithms Scheduling Real-time systems Complexity theory Resource management Trajectory optimization Convergence

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Spanners in Hypergraphs

Spanners in Hypergraphs

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IEEE International Conference on High Performance Computing Workshops (HiPCW)

作者： Maulein Pathak Yogish Sabharwal Neelima Gupta Department of Computer Science University of Delhi India Keshav Mahavidyalaya University of Delhi India IBM Research India India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

In this work, we explore parallel construction of spanners in hypergraphs. Spanners include all vertices and a subset of edges while ensuring shortest distances within an allowable stretch, making them crucial for efficiently approximating graph structures. We build on the state-of-the-art algorithm for parallel graphs spanners and extend it for hypergraphs focusing on optimizing the size without compromising stretch. We propose techniques to address memory and performance challenges when dealing with hypergraphs.

关键词： High performance computing Conferences Focusing approximation algorithms

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IUS-MAAC: Multi-Agent Attention-Critic for Task Offloading in Integrated User-Server Mobile Edge Computing

IUS-MAAC: Multi-Agent Attention-Critic for Task Offloading i...

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Chinese Automation Congress (CAC)

作者： Liang Zhu Shanxin Zhang Linbo Xie School of Internet of Things Engineering Jiangnan University Jiangsu China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In recent years, the rapid development of computation-intensive mobile applications has posed significant challenges to the computational capabilities of user devices. In response to these challenges, Mobile Edge Computing (MEC), as an emerging technology, effectively alleviates the computational pressure on user devices. Task offloading, as the core mechanism of MEC, has become a focal point in current research. Deep Reinforcement Learning (DRL) has demonstrated its unique advantages in handling task offloading. However, current DRL-based task offloading methods primarily focus on the user level, considering factors such as limited battery life and computing capacity, thereby neglecting server performance. These methods often assume that servers have sufficient computational power and storage resources. Additionally, traditional DRL performs well in handling discrete or continuous actions but still faces challenges when dealing with hybrid action spaces. Based on these, the paper proposes a novel Integrated User-Server Multi-Agent Attention-Critic (IUS-MAAC) algorithm for MEC task offloading. This takes into account various constraints during the task offloading process and demonstrates better performance in handling hybrid spaces compared to existing Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and heuristic algorithms. Based on our verification, the results show that the proposed algorithm has better convergence and improves the overall reward value by approximately 9.3% compared to other state-of-the-art algorithms.

关键词： Multi-access edge computing Automation Heuristic algorithms approximation algorithms Deep reinforcement learning Mobile applications Batteries Servers Faces Convergence

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