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The 20-nm Skyrmion Generated at Room Temperature by Spin-Orbit Torques

Chinese Physics Letters 2022年第1期39卷 40-45页

作者： Jiahao Liu Zidong Wang Teng Xu Hengan Zhou Le Zhao Soong-Guen Je Mi-Young Im Liang Fang Wanjun Jiang State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Frontier Science Center for Quantum Information Tsinghua UniversityBeijing 100084China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Center for X-ray Optics Lawrence Berkeley National LaboratoryCyclotron RoadBerkeleyCA 94720USA Department of Physics Chonnam National UniversityGwangju61186Republic of Korea

The discovery of magnetic skyrmions provides a promising pathway for developing functional spintronic memory and logic devices. Towards the future high-density memory application, nanoscale skyrmions with miniaturized diameters, ideally down to 20 nm are required. Using x-ray magnetic circular dichroism transmission microscopy,nanoscale skyrmions are observed in the [Pt/Co/Ir]15 multilayer at room temperature. In particular, small skyrmions with minimum diameters approaching 20 nm could be generated by the current-induced spin-orbit torques. Through implementing material specific parameters, the dynamic process of skyrmion generation is further investigated by performing micromagnetic simulations. According to the simulation results, we find that both the tube-like Neel-type skyrmions and the bobber-like Neel-type skyrmions can be electrically generated. In particular, the size of the bobber-like Neel-type skyrmions can be effectively reduced by the spin-orbit torques,which leads to the formation of 20 nm Neel-type skyrmions. Our findings could be important for understanding the formation dynamics of nanoscale Neel-type spin textures, skyrmions and bobber in particular, which could also be useful for promoting nanoscale skyrmionic memories and logic devices.

关键词： temperature diameters approaching

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High Performance computing for CS-InSAR Data Processing

High Performance Computing for CS-InSAR Data Processing

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2021 SAR in Big Data Era, BIGSARDATA 2021

作者： Zhang, Weikang You, Haihang Tang, Yixian Wang, Chao Zhang, Hong Chinese Academy of Sciences State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China Chinese Academy of Sciences Key Laboratory of Digital Earth Science Aerospace Information Research Institute Beijing China

ISBN: (纸本)9781665401234

In this paper, we have developed a highperformance data processing system for big SAR data based on parallel and distributed computation using supercomputer. Aiming at the pipeline of InSAR, we mainly focus on the parallelization and optimization of computing hotspot to accelerate the operation. Furthermore, we take advantage of the technology of distributed computation and multitask automation to get higher performance - process more data in less time. With the high-performance system, we obtained the nationwide measurement of China's surface movement from September 2018 to February 2020 and from January to December in 2020 using 20-30 computing nodes, processing nearly 1PB of data in total. The calculation time of the two images of national scale surface movement is completed in only 7 and 5 days, respectively. © 2021 IEEE.

关键词： Supercomputers

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DU-DARTS: Decreasing the Uncertainty of Differentiable architecture Search 32

DU-DARTS: Decreasing the Uncertainty of Differentiable Archi...

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32nd British Machine Vision Conference, BMVC 2021

作者： Lu, Shun Hu, Yu Yang, Longxing Sun, Zihao Mei, Jilin Zeng, Yiming Li, Xiaowei Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Tecent ADlab China

Differentiable Neural architecture Search (DARTS) recently attracts a lot of research attention because of its high efficiency. However, the competition of candidate operations in DARTS introduces high uncertainty for selecting the truly important operation, thus leading to serious performance collapse. In this work, we decrease the uncertainty of differentiable architecture search (DU-DARTS) by enforcing the distribution of architecture parameters to approach the one-hot categorical distribution and by replacing the zero operation with a gate switch. Without any extra search cost, our method achieves state-of-the-art performance with 2.32%, 16.74%, and 24.1% test error on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively. Moreover, DU-DARTS can robustly find an excellent architecture on NAS-Bench-1Shot1 and NAS-Bench-201, which further demonstrates the effectiveness of our method. The source code is available at https://***/ShunLu91/DU-DARTS. © 2021. The copyright of this document resides with its authors.

关键词： computer vision

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Variation Enhanced Attacks Against RRAM-based Neuromorphic computing System

arXiv

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arXiv 2023年

作者： Lv, Hao Li, Bing Zhang, Lei Liu, Cheng Wang, Ying Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China State Key Laboratory of Computer Architecture China Capital Normal University China

The RRAM-based neuromorphic computing system (NCS) has amassed explosive interests for its superior data processing capability and energy efficiency than traditional architectures, and thus being widely used in many data-centric applications. The reliability and security issues of the NCS therefore become an essential problem. In this paper, we systematically investigated the adversarial threats to the RRAM-based NCS and observed that the RRAM hardware feature can be leveraged to strengthen the attack effect, which hasn't been granted sufficient attention by previous algorithmic attack methods. Thus, we proposed two types of hardware-aware attack methods with respect to different attack scenarios and objectives. The first is adversarial attack, VADER, which perturbs the input samples to mislead the prediction of neural networks. The second is fault injection attack, EFI, which perturbs the network parameter space such that a specified sample will be classified to a target label, while maintaining the prediction accuracy on other samples. Both attack methods leverage the RRAM properties to improve the performance compared with the conventional attack methods. Experimental results show that our hardware-aware attack methods can achieve nearly 100% attack success rate with extremely low operational cost, while maintaining the attack stealthiness. Copyright © 2023, The Authors. All rights reserved.

关键词： RRAM

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Spatial-Temporal Constrained Aircraft Sequencing and Path Planning for Airport Ground Movement Problem 19th

Spatial-Temporal Constrained Aircraft Sequencing and Path P...

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19th CCF Conference on computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Liu, Fengmei Sun, Jingzhe Zhu, Jie Zhao, Zheng Huang, Huimin School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China The State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789819623754

In order to effectively alleviate the congestion and delay of aircraft during peak hours in a large hub airport, the paper studies the cooperative scheduling and path planning for arrival and departure aircrafts. A cooperative aircraft scheduling and path planning model for the airport ground movement problem is established with multiple spatial and temporal constraints, such as the queue constraint, time difference constraint, corner constraint. It aims to minimize the average taxiing time for all aircrafts. A genetic algorithm with elite selection strategy (ESGA) is proposed for the problem under study. Experimental results show that the proposed algorithm can effectively obtain the aircraft sequencing and path planning solutions, and significantly reduce the average taxiing time of the aircraft. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion planning

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Scalable parallel photonic processing unit for various neural network accelerations

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Photonics Research 2024年第11期12卷 2691-2702页

作者： SHIYIN DU JUN ZHANG HAO OUYANG ZILONG TAO QIUQUAN YAN HAO HAO JUNHU ZHOU JIE YOU YUHUA TANG TIAN JIANG State Key Laboratory of High-Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China National Innovation Institute of Defense Technology Academy of Military Sciences PLA ChinaBeijing 100071China College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha 410073China Institute for Quantum Science and Technology College of ScienceNational University of Defense TechnologyChangsha 410073China

In recent years,integrated optical processing units(IOPUs)have demonstrated advantages in energy efficiency and computational speed for neural network inference ***,limited by optical integration technology,the practicality and versatility of IOPU face serious *** this work,a scalable parallel photonic processing unit(SPPU)for various neural network accelerations based on high-speed phase modulation is proposed and implemented on a silicon-on-insulator platform,which supports parallel processing and can switch between multiple computational paradigms simply and without latency to infer different neural network structures,enabling to maximize the utility of on-chip *** SPPU adopts a scalable and process-friendly architecture design,with a preeminent photonic-core energy efficiency of 0.83 TOPS/W,two to ten times higher than existing integrated *** the proof-of-concept experiment,a convolutional neural network(CNN),a residual CNN,and a recurrent neural network(RNN)are all implemented on our photonic processor to handle multiple tasks of handwritten digit classification,signal modulation format recognition,and review emotion *** SPPU achieves multi-task parallel processing capability,serving as a promising and attractive research route to maximize the utility of on-chip components under the constraints of integrated technology,which helps to make IOPU more practical and universal.

关键词： network acceleration attractive

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Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction

arXiv

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arXiv 2025年

作者： Liu, Wenxuan Li, Zixuan Bai, Long Zuo, Yuxin Xu, Daozhu Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Geo-Information Engineering Shaanxi Xi’an China

Developing a general-purpose extraction system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the challenge comes from two aspects: 1) The absence of an efficient and effective annotation method. 2) The absence of a powerful extraction method can handle massive types. For the first challenge, we propose a collaborative annotation method based on Large Language Models (LLMs). Through collaboration among multiple LLMs, it first refines annotations of trigger words from distant supervision and then carries out argument annotation. Next, a voting phase consolidates the annotation preferences across different LLMs. Finally, we create the EEMT dataset, the largest EE dataset to date, featuring over 200,000 samples, 3,465 event types, and 6,297 role types. For the second challenge, we propose an LLM-based Partitioning EE method called LLM-PEE. To overcome the limited context length of LLMs, LLM-PEE first recalls candidate event types and then splits them into multiple partitions for LLMs to extract events. The results in the supervised setting show that LLM-PEE outperforms the state-of-the-art methods by 5.4% in event detection and 6.1% in argument extraction. In the zero-shot setting, LLM-PEE achieves up to 12.9% improvement compared to mainstream LLMs, demonstrating its strong generalization capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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Simultaneously Learning Syntactic Dependency and Semantics Reasonability for Relation Extraction

Simultaneously Learning Syntactic Dependency and Semantics R...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Wang, Xin Yin, Nan Zhang, Xiang Bai, Xinyi Luo, Zhigang College of Computer National University of Defense Technology Changsha China Institute for Quantum and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789811669620

Relation extraction as an important Natural Language Processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to capture syntactic features and achieved attractive performance. However, most existing dependency-based approaches ignore the positive influence of the words outside the dependency trees, sometimes conveying rich and useful information on relation extraction. In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. To be specific, relative position self-attention obtains the overall semantic pairwise correlation related to word position, and contextualized graph convolutional networks capture rich intra-sentence dependencies between words by adequately pruning operations. In this way, our proposed model not only reduces the noisy impact from dependency trees but also obtains easily-ignored entity-related semantic representation. Extensive experiments demonstrate that our model achieves encouraging performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Modeling the failures of power-aware data centers by leveraging heat recirculation

Modeling the failures of power-aware data centers by leverag...

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作者： Feng, Hao Deng, Yuhui Yu, Liang Department of Computer Science Jinan University Guangzhou China Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

With the explosive growth of data, hundreds of thousands of servers may be contained in a single data center. Hence, node failures are unavoidable and generally negatively effects the performance of the whole data center. Additionally, data centers with a large number of nodes will cause plenty of energy consumption. Many existing task scheduling techniques can effectively reduce the power consumption in data centers by considering heat recirculation. However, the traditional techniques do not take the situation of node failures into account. This paper proposes an airflow-based failure model for data centers by leveraging heat recirculation. In this model, the spatial distribution and time distribution of failures are considered. Furthermore, a genetic algorithm (GA) and a simulated annealing algorithm (SA) are implemented to evaluate the proposed failure model. Because the positions of node failures have a significant impact on the heat recirculation and the energy consumption of data centers, failures with different positions are analyzed and evaluated. The experimental results demonstrate that the energy consumption of data centers can be significantly reduced by using the GA and SA algorithms for task scheduling based on the proposed failure model. © 2019 John Wiley & Sons, Ltd.

关键词： Energy efficiency

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Adaptive energy-preserving algorithms for guiding center system

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Plasma Science and technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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