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SSRN 2024年

作者： Tao, Qiqi Li, Liying Zhou, Junlong Cao, Guitao Meng, Dan Shanghai Key Laboratory of Trustworthy Computing MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai200062 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

The performance of modular multiplication directly influences the efficiency of a majority of encryption ***, there are numerous FPGA-based acceleration techniques targeting modular ***, many of these implementations require substantial hardware resources or suffer from resource ***, we present a novel FPGA-based implementation of Montgomery Modular Multiplication aimed at addressing these *** design employs a suitable radix bit width and word size based on the digital signal processing (DSP) bit width rather than the conventional binary powers of *** aim to instantiate more modular multipliers using limited resources while minimizing *** also introduce a novel DSP cascade structure, called parallel grouping cascade DSP, which reduces the number of clock cycles of internal *** balance the ratio of lookup table (LUT) and DSP usage, we also use multipliers implemented in the LUT to replace some *** results, implemented on Xilinx Virtex-7 field-programmable gate array (FPGA), demonstrate more than 27\% improvement in throughput on 1024-bit modular multiplication and more than 70\% improvement on 2048-bit compared to the best previous state-of-the-art references. © 2024, The Authors. All rights reserved.

关键词： Digital signal processing

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MSTC^∗:Multi-robot coverage Path Planning under Physical constrain

MSTC<sup>∗</sup>:Multi-robot Coverage Path Planning under P...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Jingtao Tang Chun Sun Xinyu Zhang Shanghai Key Laboratory of Trustworthy Computing Engineering Research Center of Software/Hardware Co-Design Technology and Application (MoE) and School of Software Engineering East China Normal University Shanghai

For large-scale tasks, coverage path planning (CPP) can benefit greatly from multiple robots. In this paper, we present an efficient algorithm MSTC∗ for multi-robot coverage path planning (mCPP) based on spiral spanni... 详细信息

For large-scale tasks, coverage path planning (CPP) can benefit greatly from multiple robots. In this paper, we present an efficient algorithm MSTC^∗ for multi-robot coverage path planning (mCPP) based on spiral spanning tree coverage (Spiral-STC). Our algorithm incorporates strict physical constraints like terrain traversability and material load capacity. We compare our algorithm against the state-of-the-art in mCPP for regular grid maps and real field terrains in simulation environments. The experimental results show that our method significantly outperforms existing spiral-STC based mCPP methods. Our algorithm can find a set of well-balanced workload distributions for all robots and therefore, achieve the overall minimum time to complete the coverage.

关键词： Spirals Automation conferences Path planning Task analysis Robots Load modeling

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Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning

Brief Industry Paper: Towards Efficient Task Scheduling for ...

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Real-Time Systems Symposium (RTSS)

作者： Yanxing Yang Nan Zhang Dengke Yan Xian Wei Junlong Zhou Hong Liu Mingsong Chen MoE Engineering Research Center of SW/HW Co-Design Technology and Application East China Normal University School of Computer Science and Engineering Nanjing University of Science and Technology Shanghai Uni-Sentry Intelligent Technology Co. Ltd. China

As a standardized software framework and open E/E system architecture, the AUTomotive Open System ARchitecture (AUTOSAR) has been widely applied to autonomous driving systems to enable real-time control. However, due to the increasing design complexity and the lack of efficient algorithms and design automation tools, it is difficult to quickly figure out an optimal task scheduling scheme for an AUTOSAR-based system. To address this problem, we introduce a novel task scheduling method that can parallelly search for an optimal solution with the help of our proposed pruning strategy. Experimental results on a real-world AUTOSAR-based autonomous driving system demonstrate that our approach can achieve much better task scheduling solutions than the ones obtained manually and significantly reduce the overall task scheduling time.

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Federated Reinforcement Learning for Electric Vehicles Charging control on Distribution Networks

arXiv

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arXiv 2023年

作者： Qian, Junkai Jiang, Yuning Liu, Xin Wang, Qiong Wang, Ting Shi, Yuanming Chen, Wei The MoE Engineering Research Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University China The Automatic Control Laboratory EPFL Switzerland The School of Information Science and Technology ShanghaiTech University China The State Grid Beijing Electric Power Company China The Department of Electronic Engineering Tsinghua University China

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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An acceleration method of optical flow calculation for automotive ADAS

An acceleration method of optical flow calculation for autom...

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IEEE International conference on Big Data and Cloud computing (BdCloud)

作者： Wenjie Chen Ruixin Sun Zhilei Chai MoE Engineering Research Center for Software/Hardware Co-design Technology East China Normal University Shanghai China School of artificial Intelligence and computer science Jiangnan University Wuxi China

An essential component of advanced driver assistance systems (ADAS) is the real-time processing of various image types that are perceived by multiple sources. Although the optical flow is a crucial tool for determining how the camera (or vehicle) is moving in relation to its surroundings, due to the amount of computation involved, it is challenging to use it in real-time. Some of the existing fast optical flow calculation techniques are typically run on a server, which is large, expensive, and consumes a lot of power, making it difficult to mount on a real vehicle. This paper proposes an optimization technique appropriate for embedded GPU s mounted on vehicles that targets the pyramid LK optical flow method. A new dynamic window design strategy based on GPU architecture is proposed. By localizing the feature extraction algorithm to reduce the time-consuming sorting stage, improving the bidirectional constraint evaluation, and ending the iteration as soon as possible, the computational bit width is reduced, improving overall performance. Experiments with a real vehicle scene in the simultaneous localization and mapping (SLAM) system show that our approach meets the real-time requirement with 30 frames per second and improves performance by 3.8 times compared to the GPU-optimized version of OpenCV.

关键词： Point cloud compression Simultaneous localization and mapping Graphics processing units Feature extraction Cameras Real-time systems Vehicle dynamics

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Multi-granularity Information Flow Enhanced Skeleton-based Infant Seizure Detection

Multi-granularity Information Flow Enhanced Skeleton-based I...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Huiyuan Chen Weiting Chen Yiqing Zhang Na Li MOE Research Center of Software/Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Children’s Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

Automatic video seizure detection can assist in the diagnosis of seizures. Existing works usually rely on the information of single granularity for learning seizure patterns and the performance of these works is often constrained by the limited annotated data. To overcome these problems, we introduce a multi-granularity information flow method to enhance skeleton-based infant seizure detection from different perspectives. A forward-backward consistency constraint is proposed to use the reverse temporal sequences as augmented data, helping make better predictions. Besides, an importance-aware module is proposed to adaptively pay more attention to important frames. Experimental results indicate that our method can improve the performance of seizure detection and achieve high performance with a relatively low amount of parameters.

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Model Checking Quantum continuous-Time Markov Chains 32

Model Checking Quantum Continuous-Time Markov Chains

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32nd International conference on concurrency Theory, coNCUR 2021

作者： Xu, Ming Mei, Jingyi Guan, Ji Yu, Nengkun MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783959772037

Verifying quantum systems has attracted a lot of interests in the last decades. In this paper, we initialise the model checking of quantum continuous-time Markov chain (QCTMC). As a real-time system, we specify the temporal properties on QCTMC by signal temporal logic (STL). To effectively check the atomic propositions in STL, we develop a state-of-the-art real root isolation algorithm under Schanuel's conjecture;further, we check the general STL formula by interval operations with a bottom-up fashion, whose query complexity turns out to be linear in the size of the input formula by calling the real root isolation algorithm. A running example of an open quantum walk is provided to demonstrate our method. © Ming Xu, Jingyi Mei, Ji Guan, and Nengkun Yu;.

关键词： Model checking

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DSAM-GN:Graph Network based on Dynamic Similarity Adjacency Matrices for Vehicle Re-identification

arXiv

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arXiv 2023年

作者： Jiao, Yuejun Qiu, Song Chen, Mingsong Han, Dingding Li, Qingli Lu, Yue MOE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China School of Information Science and Technology Fudan University Shanghai200433 China Shanghai Artificial lntelligence Laboratory Shanghai200232 China

In recent years, vehicle re-identification (Re-ID) has gained increasing importance in various applications such as assisted driving systems, traffic flow management, and vehicle tracking, due to the growth of intelligent transportation systems. However, the presence of extraneous background information and occlusions can interfere with the learning of discriminative features, leading to significant variations in the same vehicle image across different scenarios. This paper proposes a method, named graph network based on dynamic similarity adjacency matrices (DSAM-GN), which incorporates a novel approach for constructing adjacency matrices to capture spatial relationships of local features and reduce background noise. Specifically, the proposed method divides the extracted vehicle features into different patches as nodes within the graph network. A spatial attention-based similarity adjacency matrix generation (SASAMG) module is employed to compute similarity matrices of nodes, and a dynamic erasure operation is applied to disconnect nodes with low similarity, resulting in similarity adjacency matrices. Finally, the nodes and similarity adjacency matrices are fed into graph networks to extract more discriminative features for vehicle Re-ID. Experimental results on public datasets VeRi-776 and VehicleID demonstrate the effectiveness of the proposed method compared with recent works. copyright © 2023, The Authors. All rights reserved.

关键词： Intelligent systems

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Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT applications

arXiv

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arXiv 2022年

作者： Liu, Tian Ding, Jiahao Wang, Ting Pan, Miao Chen, Mingsong The MoE Engineering Research Center of Software Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China The Department of Information Science and Engineering Zaozhuang University Zaozhuang277160 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States

As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL) is becoming popular in Internet of Things (IoT) design. However, when the data collected by IoT devices are highly skewed in a non-independent and identically distributed (non-IID) manner, the accuracy of vanilla FL method cannot be guaranteed. Although there exist various solutions that try to address the bottleneck of FL with non-IID data, most of them suffer from extra intolerable communication overhead and low model accuracy. To enable fast and accurate FL, this paper proposes a novel data-based device grouping approach that can effectively reduce the disadvantages of weight divergence during the training of non-IID data. However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure. To solve this problem, we propose an improved version by exploiting similarity information using the Locality-Sensitive Hashing (LSH) algorithm without exposing extracted feature maps. comprehensive experimental results on well-known benchmarks show that our approach can not only accelerate the convergence rate, but also improve the prediction accuracy for FL with non-IID data. copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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A Genetic Algorithm for Scheduling in Heterogeneous Multicore System Integrated with FPGA

A Genetic Algorithm for Scheduling in Heterogeneous Multicor...

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IEEE International conference on Big Data and Cloud computing (BdCloud)

作者： Qingyuan Jiang Jinyi Xu Yixiang Chen East China Normal University Shanghai China East China Normal University and MOE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

The heterogeneous multicore system integrated with FPGA is a kind of Multi-Processor Systems-on-Chip(MPSoC) that can achieve both efficiency and flexibility. FPGA uses hardware resource instead of instruction sets to process tasks. The resource on FPGA is limited which should be considered when scheduling tasks. We propose a static task scheduling algorithm targeting this kind of system. The aim is to minimize the executing time of an application as well as considering FPGA resource limit. In our genetic algorithm-based method, a chromosome consists of computing units where tasks are assigned. When generating the initial population, some tasks are assigned to FPGA, considering FPGA resource limit. We have modified the crossover operator and mutation operator to ensure that the FPGA resource used implied in chromosomes will not exceed the FPGA resource limit. Task scheduling is completed in the chromosome evaluation stage. Through the genetic algorithm, the improved task assignment and schedule sequence are obtained. The experiments on random graph applications and two real-world applications show that our method has achieved better performance than existing works.

关键词： Schedules Multicore processing Scheduling algorithms Sociology Scheduling Task analysis Statistics

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