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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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t-PSI: Efficient Multi-party Private Set Intersection with Threshold 19

t-PSI: Efficient Multi-party Private Set Intersection with T...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Meng, Dan Fu, Zhihui Kong, Chao Qi, Yue Cao, Guitao Oppo Research Institute Shenzhen China Anhui Polytechnic University School of Computer and Information Wuhu China East China Normal University Shanghai Key Laboratory of Trustworthy Computing Shanghai China MoE Engineering Research Center of SH/HW Co-design Technology and Application Ecnu Shanghai China

ISBN: (纸本)9798350346558

Multi-party privacy set intersection (MPSI) enables multiple parties to compute the intersection of their datasets without leaking data privacy. Among existing MPSI protocols, e.g., KMPRT-based protocols, oblivious evaluation of programmable pseudo-random function (OPPRF) or oblivious pseudo-random function (OPRF) is typically used to generate pseudo random numbers, requiring frequent online interactions. To mitigate such communication overheads, we propose the threshold Privacy Set Intersection (t-PSI) protocol for multi-party, leveraging the Shamir Secret Sharing (SSS) protocol and the masking construction mechanism. We propose two types of masking construction mechanisms: Polynomial and Garbled Bloom-filter. To the best of our knowledge, this is the first Secret Sharing-based MPSI protocol. With the significant reduction of inter-party communication, the t-PSI protocol is highly scalable and can handle datasets ranging from 26 to 220. Further, with built-in fault tolerance, t-PSI works even if some parties went offline during protocol execution. As shown in the experiments, for five parties with datasets of 220 items each, the proposed t-PSI takes only 63 seconds, outperforming the state-of-the-art in [1]. © 2022 IEEE.

关键词： Fault tolerance

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When Adversarial Example Attacks Meet Vertical Federated Learning 19

When Adversarial Example Attacks Meet Vertical Federated Lea...

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ISBN: (纸本)9798350346558

Federated learning (FL) is proposed to enable efficient machine learning while protecting the privacy of user data. In non-federated scenarios, machine learning models are vulnerable to adversarial attacks. The attacker can construct elaborate adversarial examples, which can greatly reduce the performance of the victim model. It is worth exploring how FL models differ from traditional models in face of complex adversarial attacks. We are also curious about the robustness of different FL modeling methods against adversarial attacks, providing suggestions for selecting suitable FL models in practical applications. Consequently, we perform adversarial example attacks on vertical federated learning and design experiments to study how vertical federated models perform under adversarial example attacks. The simulation results show that federated models are more robust than non-federated models. Besides, in vertical federation learning, decision trees and XGBoost are much more robust than logistic regression and neural networks. © 2022 IEEE.

关键词： Decision trees

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Continuous Geodesic Self-Attention Models with Gated Fusion for Trajectory Prediction

Continuous Geodesic Self-Attention Models with Gated Fusion ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kexin Ke Zhengyu Li Huining Chen Hao Wang Xian Wei Jian Yang Xuan Tang MoE Engineering Research MOE Center of SW/HW Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University China School of Advanced Technology Xi’an Jiaotong-Liverpool University China School of Geospatial Information Information Engineering University China School of Communications and Electronic Engineering East China Normal University China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Driven by the rapid development of intelligent driving vehicles, predicting the trajectories of pedestrians on the road is crucial for decision-making during driving and even road safety. In this paper, we propose a novel method for trajectory prediction, namely, Continuous Geodesic Self-Attention Models with Gated Fusion (CGSAG). We use geodesic attention to measure the similarity between trajectory points, and utilize a gating mechanism to fuse the geodesic features extracted by multi-layer graph convolution. We then use Neural Ordinary Differential Equations (Neural ODE) to model the continuous-time dynamics of the trajectory. We show that CGSAG improves state-of-the-art performances on several human trajectory prediction datasets, including ETH/UCY, SDD, and Ind. At the same time, we conduct ablation studies to prove the effectiveness and efficiency of our proposed method.

关键词： Pedestrians Logic gates Predictive models Feature extraction Mathematical models Time measurement Road safety

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Prophet: SSD Failure Analysis and Prediction Guided by Flash Reliability Characteristics in Data centers

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IEEE Transactions on computers 2025年

作者： Song, Yunpeng Liang, Yujiong Liu, Jialin Shi, Liang East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China East China Normal University School of Computer Science and Technology Shanghai China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan China

Solid-state drives (SSDs) are massively deployed in various fields, especially in data centers, for their excellent cost-effectiveness. However, SSDs may fail due to their imperfect manufacturing processes, resulting in system-level failures and even downtime in data centers. This makes SSD failure prediction critical. Current studies focus on dealing with data missing, numerical normalization, and other statistical issues in using machine learning methods, but the consideration of the reliability characteristics of the underlying flash media of SSDs and the timeliness (time duration between predicted failure and real failure) of SSD failure prediction result is missing. In this work, we study the failure characteristics of over 200,000 drives from industry data centers over a 4-year period, as well as daily data. The relationship between SSD attribute values and failures is first investigated. Then, we analyzed the SSD failure characteristics from several aspects (causes, differences between failures, and timeliness of prediction results) relying on flash reliability characteristics. Based on these, a novel SSD failure prediction method (Prophet) is proposed. Specifically, Prophet contains the following two components. First, to cope with the differences between failures, a diff-state method is proposed for differential machine learning modeling of SSDs in different "States". We define the "State" of an SSD, which represents the range of values in which the SSD currently lies in terms of some key attributes. Through flash reliability characteristics, we distinguish between different failures before training the model to obtain accurate predictions of different failure behaviors. Second, a recovery period method is proposed to enhance the timeliness of SSD failure prediction result by designing the sample selection method. The enhanced timeliness can be utilized by operations personnel to handle failed SSDs, such as replacement and repair. The evaluation results of

关键词： Failure rate

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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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RAID45: Hybrid Parity-Based RAID for Reducing Parity Write Wear on High-Density SSDs

RAID45: Hybrid Parity-Based RAID for Reducing Parity Write W...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Jialin Liu Yujiong Liang Yunpeng Song Liang Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

High-densitysolid-state drives (SSDs), such as triple-level cell (TLC) or quad-level cell (QLC) flash, are adopted in parity-based RAID systems to achieve high reliability with low redundancy. However, the parity writes cause high write wear, which is unfriendly to such high-density SSDs with low write endurance. Conversely, high-performance SSDs, such as ZNAND, XL-Flash, have high write endurance but their high cost per bit hinders their deployment in RAID. This paper proposed a novel hybrid RAID structure, RAID45, to reduce parity writes for highdensity SSDs. Specifically, RAID45 uses high-performance SSD to store the parity of write-intensive stripes to absorb as much of the wear of parity writes on high-density SSDs as possible. Experimental results on real platform show that RAID45 achieves encouraging parity write reduction on the high-density SSDs.

关键词： Costs Redundancy Reliability

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Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

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Towards Efficient Workflow Scheduling Over Yarn Cluster Using Deep Reinforcement Learning

Towards Efficient Workflow Scheduling Over Yarn Cluster Usin...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jianguo Xue Ting Wang Puyu Cai MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Department of Computer Science and Engineering Michigan State University USA

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For instance, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have available resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.

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Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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