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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Wenhui Zhang Qiang Cao Hong Jiang Jie Yao Yuanyuan Dong Puyuan Yang Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering University of Texas at Arlington Arlington Texas School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Alibaba Group. Hangzhou China

To address the write performance problem suffered by MLC/TLC flash, researchers have proposed hybrid SSD that aims to combine the strengths of SLC flash, used as the write-buffer zone for its superior write performance, and MLC/TLC flash, as the capacity zone for its high storage density. While leveraging SLC as a physical write-buffer zone is proven effective in traditional 2D hybrid SSDs, how to effectively incorporate SLC into a 3D-stacked TLC to form a hybrid SSD has not been studied to the best of our knowledge. Yet this is a timely and important performance issue for 3D-stacked TLC given its one-shot programming scheme that results in much worse write performance than the programming scheme in 2D TLC where pages are associated with different bits of a cell and programmed in sequence separately. We believe that naively adopting the two-physical-zone approach to 3D hybrid SSD will miss a great opportunity for performance optimization because it ignores the inherent four-level parallelism (channel/chip/die/plane) of the flash chip array. To this end, we propose in this paper an SLC and Parallelism Aware hybrid SSD (SPA-SSD) to take full advantages of SLC's superior write performance, the internal multi-level parallelism of SSD, and the high storage density of 3D-stacked TLC flash. Two novel techniques enable SPA-SSD to be highly effective: (1) Type-Parallelism Joint Page Allocation (TPJ-PA), which allocates pages for write transactions according to not only available SLC pages but also parallelism to maximize resource utilization within the hybrid SSD, and (2) Queue-length and Parallelism Constrained data Migration (QPC-DM), which triggers data migration without degrading user write performance by analyzing the device queue length and available flash resources. To evaluate performance of SPA-SSD, a hybrid SSD simulator, called HybridSim, is developed based on MQSim. Experimental results on HybridSim show that TPJ-PA improves write throughput by 60%, while QPC-DM

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Quaternary Neural Network Equalization for Three-Dimentional Magnetic Recording

Quaternary Neural Network Equalization for Three-Dimentional...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Ke Luo Yufei Wu Yanzhe Liao Shaobing Wang Yugen Jian Jincai Chen Ping Lu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China School of Public Health Hubei University of Medicine Shiyan China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

One approach considered promising for advancing the development of magnetic data storage systems is three-dimensional magnetic recording. But when the recording density rises, media noise becomes a bigger problem for magnetic recording technology. Machine learning algorithms have been proven to outperform traditional numerical techniques in solving the magnetic recording signal quality issue. We compared the detection bit error rate (BER) performance of a per-layer shallow neural network equalizer and a quaternary neural network equalizer for two recording layers. For a two-layer recording density of 4 Tb/in 2 , the results show that the combined quaternary neural network equalization and detection scheme outperforms the per-layer shallow neural network equalization and one-dimensional Viterbi detection and outperforms the conventional two-dimensional generalized partial response equalizer combined with a quaternary Viterbi detector, especially for the bottom layer.

关键词： Machine learning algorithms Equalizers Viterbi algorithm Buffer storage Neural networks Bit error rate Noise

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Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval

arXiv

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arXiv 2022年

作者： Fang, Xiang Liu, Daizong Zhou, Pan Hu, Yuchong Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Wangxuan Institute of Computer Technology Peking University No. 128 Zhongguancun North Street Beijing China School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China

As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. Most previous methods depend heavily on numerous manual annotations (i.e., moment boundaries), which are extremely expensive to acquire in practice. In addition, due to the domain gap between different datasets, directly applying these pre-trained models to an unseen domain leads to a significant performance drop. In this paper, we focus on a novel task: cross-domain VMR, where fully-annotated datasets are available in one domain ("source domain"), but the domain of interest ("target domain") only contains unannotated datasets. As far as we know, we present the first study on cross-domain VMR. To address this new task, we propose a novel Multi-Modal Cross-Domain Alignment (MMCDA) network to transfer the annotation knowledge from the source domain to the target domain. However, due to the domain discrepancy between the source and target domains and the semantic gap between videos and queries, directly applying trained models to the target domain generally leads to a performance drop. To solve this problem, we develop three novel modules: (i) a domain alignment module is designed to align the feature distributions between different domains of each modality;(ii) a cross-modal alignment module aims to map both video and query features into a joint embedding space and to align the feature distributions between different modalities in the target domain;(iii) a specific alignment module tries to obtain the fine-grained similarity between a specific frame and the given query for optimal localization. By jointly training these three modules, our MMCDA can learn domain-invariant and semantic-aligned cross-modal representations. Extensive experiments on three challenging benchmarks (ActivityNet Captions, Charades-STA and TACoS) illustrate that our cross-domain method MMCDA outperforms a

关键词： Alignment

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On the Impact of Interlayer Misalignment for Dual-Layer data Detection in Three Dimensional Magnetic Recording

SSRN

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SSRN 2024年

作者： Luo, Ke Liao, Yanzhe Zhang, Kezheng Jian, Yugen Wang, Shaobing Chen, Jincai Lu, Ping Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China School of Public Health Hubei University of Medicine Shiyan442000 China

Three-dimensional magnetic recording (3DMR) is a crucial technology for significantly increasing the storage capacity of hard disk drives (HDDs). However, the presence of inter-symbol interference (ISI), inter-track interference (ITI), and inter-layer interference (ILI) poses significant challenges to the accurate detection of data stored on multiple layers. This study addresses the impact of interlayer misalignment on the bit error rate (BER) performance in 3DMR systems. We evaluate the performance based on a neural network estimator for reconstructing the top-layer read response signal using feedback from a Viterbi detector, enabling the separation of bottom-layer signals by subtraction from the mixed readback signal. We introduce a dual-layer partial response maximum likelihood (PRML) detector for simultaneous bit retrieval from both layers. We investigate methods of a per-layer binary classifier and a dual-layer four-class classifier based on neural networks. Our study demonstrates the BER performance of these detection schemes influenced by the interlayer misalignment, especially when the offset is 0, 10%, 50%, and 90% of the bit dimensions between the two layers. The results show that the neural network-based reconstruction and separation method achieves better bottom-layer BER performance under a slight interlayer misalignment. The BER performance benefits more from a mild downtrack offset compared with the crosstrack offset. Both the separation detection based on a neural network and the dual-layer PRML achieve the lowest top-layer BER and the worst bottom-layer BER when the interlayer misalignment is 50% of a bit size. © 2024, The Authors. All rights reserved.

关键词： Bit error rate

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storage management mechanism for a scalable streaming media system

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WSEAS Transactions on Computers 2006年第4期5卷 746-752页

作者： Deng, Yuhui Wang, Frank Feng, Dan Helian, Na Milward, Dave Thompson, Steve Center for Grid Computing Cambridge-Cranfield High Performance Computing Facilities Cranfield University Campus Bedfordshire MK43 0AL United Kingdom Key Laboratory of Data Storage System Ministry of Education Huazhong University of Science and Technology Wuhan 430074 China London Metropolitan University London United Kingdom Xyratex United Kingdom

Streaming media is pervasive on the Internet now and continues to grow rapidly. A large scale streaming media system may demand thousands of disks to satisfy both the bandwidth and storage capacity requirements imposed by thousands of concurrent clients. While performance has long been the focus of large scale storage systems research, manageability has become the dominant criterion in evaluating storage solutions, as the cost of storage management outweighs the cost of the storage devices themselves by a factor of three to eight. This paper designs and implements a highly reliable and available distributed media data oriented innovative storage Management Mechanism (SMM) in a scalable streaming media system. All heterogeneous storage resources across the system are aggregated as a single logical view which strikes a good balance between a distributed data storage and centralized storage management. The centralized SMM dramatically simplifies the storage administration as the size and complexity of the streaming media system grows. Fault tolerance and dynamic load balance at the disk level and system level are achieved to provide high reliability and availability due to the SMM.

关键词： Multimedia systems

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All-electric mimicking synaptic plasticity based on the noncollinear antiferromagnetic device

arXiv

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arXiv 2024年

作者： Cao, Cuimei Duan, Wei Feng, Xiaoyu Xu, Yan Wang, Yihan Yang, Zhenzhong Zhan, Qingfeng You, Long School of Integrated Circuits Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China School of Physics and Electronic Science East China Normal University Shanghai200241 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

Neuromorphic computing, which seeks to replicate the brain’s ability to process information, has garnered significant attention due to its potential to achieve brain-like computing efficiency and human cognitive intelligence. Spin-orbit torque (SOT) devices can be used to simulate artificial synapses with non-volatile, high-speed processing and endurance characteristics. Nevertheless, achieving energy-efficient all-electric synaptic plasticity emulation using SOT devices remains a challenge. We chose the noncollinear antiferromagnetic Mn3Pt as spin source to fabricate the Mn3Pt-based SOT device, leveraging its unconventional spin current resulting from magnetic space breaking. By adjusting the amplitude, duration, and number of pulsed currents, the Mn3Pt-based SOT device achieves nonvolatile multi-state modulated by all-electric SOT switching, enabling emulate synaptic behaviors like excitatory postsynaptic potential (EPSP), inhibitory postsynaptic potential (IPSP), long-term depression (LTD) and the long-term potentiation (LTP) process. In addition, we show the successful training of an artificial neural network based on such SOT device in recognizing handwritten digits with a high recognition accuracy of 94.95 %, which is only slightly lower than that from simulations (98.04 %). These findings suggest that the Mn3Pt-based SOT device is a promising candidate for the implementation of memristor-based brain-inspired computing systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Multicarrier modulation

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Exploiting Minipage-Level Mapping to Improve Write Efficiency of NAND Flash

Exploiting Minipage-Level Mapping to Improve Write Efficienc...

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International Conference on Networking, Architecture, and storage (NAS)

作者： Hao Lv You Zhou Fei Wu Weijun Xiao Xubin He Zhonghai Lu Changsheng Xie Shenzhen research institute of Huazhong University of Science and Technology Shenzhen China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Ministry of Education Key Laboratory of Data Storage System Wuhan China Department of Electrical and Computer Engineering Virginia Commonwealth University Richmond USA College of Science and Technology Temple University Philadelphia USA School of Information and Communication Technology KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781538683682

Pushing NAND flash memory to higher density, manufacturers are aggressively enlarging the flash page size. However, the sizes of I/O requests in a wide range of scenarios do not grow accordingly. Since a page is the unit of flash read/write operations, traditional flash translation layers (FTLs) maintain the page mapping regularity. Hence, small random write requests become common, leading to extensive partial logical page writes. This write inefficiency significantly degrades the performance and increases the write amplification of flash storage. In this paper, we first propose a configurable mapping layer, called minipage, whose size is set to match I/O request sizes. The minipage-level mapping provides better flexibility in handling small writes at the cost of sequential read performance degradation and a larger mapping table. Then, we propose a new FTL, called PM-FTL, that exploits the minipage-level mapping to improve write efficiency and utilizes the page-level mapping to reduce the costs caused by the minipage-level mapping. Finally, trace-driven simulation results show that compared to traditional FTLs, PM-FTL reduces the write amplification and flash storage response time by an average of 33.4% and 19.1%, up to 57.7% and 34%, respectively, under 16KB flash pages and 4KB minipages.

关键词： Flash memories Random access memory Metadata Electronic mail Time factors Memory management Market research

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Unbalanced incomplete multi-view clustering via the scheme of view evolution: Weak views are meat;strong views do eat

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem;2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments in https://***/ZeusDavide/TETCI_UIMC. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Virtual storage Image Implementation in a SAN system to Improve storage Capacity, Fault Tolerance and Bandwidth

Virtual Storage Image Implementation in a SAN System to Impr...

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Intermag 2006 (INTERMAG 2006), vol.2

作者： Y. Deng F. Wang K. Zhou S. Wu Center for Grid Computing Cambridge-Cranfield High Performance Computing Facility Cranfield Bedfordshire UK Key Laboratory of Data Storage System Ministry of Education School of Computer Huazhong University of Science and Technology Wuhan China

With a SAN (storage Area Network), large-capacity storage can be shared among multiple hosts at high speed. The potential market for SAN is enormous, but the technology won't become ubiquitous overnight. The complex administration is indicated by many researches as a key barrier to adopt SAN solutions. This paper designs and implements a virtual storage image (VSI) that aggregates storage space located on heterogeneous storage nodes (see Fig.1)in a Fibre Channel point-to-point (FC-P2P) SAN.

关键词： Image storage storage area networks Fault tolerant systems Bandwidth File systems Space technology Aggregates Linux Fault tolerance Load management

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ANIMC: A soft approach for auto-weighted noisy and incomplete multi-view clustering

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multi-view clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multi-view clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering approach (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model. Firstly, by designing adaptive semi-regularized nonnegative matrix factorization (adaptive semi-RNMF), the soft auto-weighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Secondly, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results demonstrate its superior advantages over other state-of-the-art methods. Impact Statement-As an effective method to process data from multiple sources, multi-view clustering has attracted more and more attention. However, most previous works ignore missing instances and noises in original multi-view data, which limits the applications of these works. By a soft approach, our proposed ANIMC can effectively reduce the negative influence of missing instances and noises. Moreover, ANIMC outperforms the stateof- the-art works by about 20% in representative cases. With satisfactory performance on multiple rea

关键词： Regression analysis

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