检索结果-内蒙古大学图书馆

61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Huang, Haoqin Yao, Pengcheng An, Zhaozeng Sun, Yufei Hu, Ao Xu, Peng Zheng, Long Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9798400706011

Sparse general matrix-matrix multiplication is widely used in data mining applications. Its irregular memory access patterns limit the performance of general-purpose processors, thus motivating many FPGA-based hardware innovations in recent years. Nevertheless, existing accelerators fail to efficiently support heterogeneous input matrix sparsity, which is universal in various real-world applications. With in-depth experimental analysis, we observe that their performance is bottlenecked by their fixed tiling mechanisms, which only alleviate the irregularity of one input matrix. Based on the observation, we propose SpaHet, a software/hardware co-design to accelerate heterogeneous-sparsity based sparse matrix multiplication. SpaHet adopts a dual-adaptive sliding window mechanism to cover the reuse characteristics of both input matrices simultaneously. With a specialized exploration algorithm, the window-based mechanism can automatically find the optimal tiling strategy instead of applying a fixed one based on empirical experience. A sparsity-aware merge tree is also proposed to maximize the output matrix reuse via accumulating intermediate results thoroughly. Our results on a Xilinx Alveo U280 accelerator card show that SpaHet outperforms state-of-the-art CPU-, GPU- and FPGA-based solutions by 7.71×, 1.1×, and 2.74× in performance, respectively. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Program processors

来源：评论

学校读者我要写书评

暂无评论

Towards Redundancy-Free Recommendation Model Training via Reusable-aware Near-Memory Processing 24

Towards Redundancy-Free Recommendation Model Training via Re...

引用

61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Liu, Haifeng Zheng, Long Huang, Yu Huang, Haoyan Liao, Xiaofei Hai, Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9798400706011

The memory-intensive embedding layer in recommendation model continues to be the performance bottleneck. While prior works have attempted to improve the embedding layer performance by exploiting the data locality to cache the frequently accessed embedding vectors and their partial sums. However, these solutions rely on the static cache, which is inapplicable in the embedding training scenario where the embedding vectors are updated frequently. To this end, this paper proposes ReFree, a redundancy-free near-memory processing (NMP) solution for recommendation model training. Specifically, ReFree identifies the reusable data in realtime for both embedding layer forward and backward stages and leverages a lightweight NMP architecture to enable redundancy-free near-memory acceleration of the entire embedding training process. Evaluation results on real-world datasets show that ReFree outperforms the state-of-the-art solutions by 10.9× and reduces 5.3× energy consumption on average. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Partitioning large-scale property graph for efficient distributed query processing 21

Partitioning large-scale property graph for efficient distri...

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and systems, HPCC/SmartCity/DSS 2019

作者： Pang, Haohan Gan, Peng Yuan, Pingpeng Jin, Hai Hua, Qiangsheng National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Clusters and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728120584

Recently, many communities have published their domain knowledge in the form of graphs. A significant trend is that the relations between pairs of nodes in the graph have become so complex that these data can be treated as property graphs. Complex properties imply semantic constraints which can distinguish elements of property graph into different semantic-related aspects. If the property graph can be partitioned according to these aspects, cross-partition communication would be reduced when evaluating SPARQL queries, for the reason that a query can often be evaluated in a single aspect. Conventional graph partition methods, however, pay little attention to semantic constraints in property graphs. To solve this problem, we propose a new property graph partition method for query optimization. First, we propose the property semantic reachable path as basic partition element, with which to capture the semantic-related aspects in a property graph. Second, to combine paths into partitions, we develop a space-efficient algorithm which merges vertices rather than entire paths. Extensive experiments over representative property graph data confirm the effectiveness of our semantic awareness approach. The performance of our approach is comparable with leading competitors, in terms of load balance, data redundancy, and network overhead. © 2019 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

引用

38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Metamorphic Testing for Traffic Sign Detection and Recognition 23

Metamorphic Testing for Traffic Sign Detection and Recogniti...

引用

23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Long, Yuanyuan Fan, Yong Pan, Ya School of Computer Science and Technology of Southwest University of Science and Technology Mianyang China Sichuan Big Data and Intelligent System Engineering Technology Research Center Mianyang China

ISBN: (纸本)9798350359398

Traffic sign detection and recognition (TSDR) is a crucial technology to realize vehicle autonomous driving and maintain road safety. Misdetection or misclassification of traffic signs can lead to serious traffic accidents. Therefore, it is essential to ensure the correctness of traffic sign detection and recognition systems. However, most existing methods use labeled data, but it isn't easy to obtain labeled data. The metamorphic testing can reduce the dependence on the labeled data, but the current metamorphic relations do not consider the domain characteristics of traffic signs, so it is difficult to effectively detect traffic sign detection and recognition systems faults. To solve the above problems, this paper introduces metamorphic testing to verify traffic sign detection and recognition systems and designs and implements a tool, MTSDR, which provides a series of metamorphic relations in the traffic domain. It can generate many follow-up test images based on these metamorphic relations and automatically performs tests for evaluation, focusing on practical failure situations. The experimental evaluation was carried out on four advanced traffic sign detection and recognition models on open datasets. The results show that MTSDR can generate natural images and find thousands of traffic sign detection and recognition faults. Compared with the previous methods, our method has a higher fault detection capability, and the highest fault detection rate reaches more than 40%. In addition, it finds that retraining the traffic sign detection and recognition models using the images that found the fault can improve the model's performance, reduce its fault, and reduce the fault rate by 11.77% on average. © 2023 IEEE.

关键词： Traffic signs

来源：评论

学校读者我要写书评

暂无评论

A data-driven approach for recommending UI element layout

引用

Science China(Information Sciences) 2020年第9期63卷 61-63页

作者： Yonghao LONG Xiangping CHEN Yuting XIE School of Information Science and Technology National Engineering Research Center of Digital LifeSun Yat-sen University Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and Design Sun Yat-sen University

Dear editor,Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. GUI design plays an important role in offering a smooth user experience. The complexity of the apps often depends on the user interface (UI)[1]with minor data processing or data processing delegates to the backend component.

关键词： UI development Software mining Layout recommendation data-driven development Intelligent Software Development

来源：评论

学校读者我要写书评

暂无评论

Can Language Models Serve as Temporal Knowledge Bases?

Can Language Models Serve as Temporal Knowledge Bases?

引用

2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Ruilin Zhao, Feng Xu, Guandong Zhang, Sixiao Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Recent progress regarding the use of language models (LMs) as knowledge bases (KBs) has shown that language models can act as structured knowledge bases for storing relational facts. However, most existing works only considered the LM-as-KB paradigm in a static setting, which ignores the analysis of temporal dynamics of world knowledge. Furthermore, a basic function of KBs, i.e., the ability to store conflicting information (i.e., 1-N, N-1 and N-M relations), is underexplored. In this paper, we formulate two practical requirements for treating LMs as temporal KBs: (i) the capacity to store temporally-scoped knowledge that contains conflicting information and (ii) the ability to use stored knowledge for temporally-scoped knowledge queries. We introduce a new dataset called LAMA-TK which is aimed at probing temporally-scoped knowledge, and investigate the two above requirements to explore the LM-as-KB paradigm in the temporal domain. On the one hand, experiments show that LMs can memorize millions of temporally-scoped facts with relatively high accuracy and transfer stored knowledge to temporal knowledge queries, thereby expanding the LM-as-KB paradigm to the temporal domain. On the other hand, we show that memorizing conflicting information, which has been neglected by previous works, is still challenging for LMs and hinders the memorization of other unrelated one-to-one relationships. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Recent advances in degraders engaging lysosomal pathways and related nanomedicine

引用

European Journal of Medicinal Chemistry 2025年 292卷 117701页

作者： Lu, Runxin Ni, Xiaofeng Diao, Sha Wu, Yong Zhang, Lingli Department of Pharmacy/Evidence-Based Pharmacy Center Key Laboratory of Birth Defects and Related Diseases of Women and Children Children's Medicine Key Laboratory of Sichuan Province NMPA Key Laboratory for Technical Research on Drug Products in Vitro and in Vivo Correlation West China Second University Hospital Sichuan University Chengdu 610041 China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu 610041 China Chinese Evidence-based Medicine Center West China Hospital Sichuan University Chengdu 610041 China Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology West China School of Pharmacy Sichuan University Chengdu 610041 China

The advent of targeted protein degradation (TPD) strategies presents unparalleled opportunities for innovating and expediting the development of new drugs. As the most mature TPD technology to date, proteolysis targeting chimeras (PROTACs) reliant on the ubiquitin proteasome system (UPS) have successfully transitioned from the laboratory to phase III clinical trials after nearly two decades of development. In recent years, the gradually emerging degraders engaging lysosomal pathways have further broadened the range of degradation mechanisms and substantially increased the diversity of potential targets and indications, ushering in a new era for the TPD field. Despite their significant advantages, the limited permeability, adverse pharmacokinetic properties, and off-target side effects caused by non-specific distribution still pose significant challenges to the clinical translation of these degraders. Currently, researchers are exploring the use of nanotechnology to surmount these obstacles and have achieved notable progress. This paper systematically summarizes the fundamental design principles, research status, challenges and future prospects of degraders engaging lysosomal pathways, and highlights the efforts and latest advances in related nanomedicine to optimize these degraders. The aim of this review is to deepen our comprehension of this emerging field and offer guidance for future exploration, development, and further utilization of new TPD techniques. © 2025 Elsevier Masson SAS

关键词： Autophagy Degraders Lysosomal pathways Nanomedicine Targeted protein degradation

来源：评论

学校读者我要写书评

暂无评论

ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

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

Multiview 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 multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, 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 show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Space-Oblivious Compression and Wear Leveling for Non-Volatile Main Memories 36

Space-Oblivious Compression and Wear Leveling for Non-Volati...

引用

36th International Conference on Massive Storage systems and technology, MSST 2020

作者： Liu, Haikun Ye, Yuanyuan Liao, Xiaofei Jin, Hai Zhang, Yu Jiang, Wenbin He, Bingsheng National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing National University of Singapore Singapore

Emerging Non-Volatile Main Memory (NVMM) technologies generally feature high density, low energy consumption and cost per bit compared to DRAM. However, the limited write endurance of NVMM poses a significant challenge of using NVMM as an substitute for DRAM. This paper proposes a space-oblivious compression and wear-leveling based memory architecture to improve the write endurance of NVMM. As memory blocks of many applications usually contain a large amount of zero bytes and frequent values, we propose a nonuniform compression encoding scheme that integrates Zero Deduplication with Frequent Value Compression (called ZD-FVC) to reduce bit-writes on NVMM. Moreover, we leverage the memory space available through compression to achieve an intra-block wear leveling policy. It rotates the writing positions of a compressed data block within the data’s initial memory space, and thus enhances write endurance by balancing the bit-writes per NVMM cell. ZD-FVC can be integrated into the NVMM module and implemented entirely by hardware, without any intervention of Operating systems. We implement ZD-FVC in Gem5 and NVMain simulators, and evaluate it with several programs from SPEC CPU2006. Experimental results shows that ZD-FVC is much better than several state-of-the-art approaches. Particularly, ZD-FVC can improve data compression ratio by 55% compared to Frequent Value Compression. Compared with the data Comparison Write, ZD-FVC is able to significantly improve the lifetime of NVMM by 3.3X on average, and also reduces NVMM write latency by 31% and energy consumption by 19% on average. © 2020 36th International Conference on Massive Storage systems and technology, MSST 2020. All Rights Reserved.

关键词： Dynamic random access storage

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：