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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Chen, Zhihao Wang, Hongya School of Computer Science and Technology Donghua University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783031466731

Currently, large-scale vision and language models has significantly improved the performances of cross-modal retrieval tasks. However, large-scale models require a substantial amount of computing resources, so the execution of these models on devices with limited resources is challenging. Thus, it is paramount to reduce the model size and minimize computing costs of a model without sacrificing its performance. In this paper, we improved TERAN by dividing cross-modal retrieval into two stages: image-text coarse-grained matching and image-text fine-grained matching. Specifically, we present a novel approach called Two-Stage Cross-Modal Retrieval network(TSCMR). To reduce model size after model training, our approach utilized a new knowledge distillation method for Transformer-based models. Experiments have shown that our approach maintains a performance comparable to TERAN on the MS-COCO 1K test set, while being 2x smaller and 3.1x faster on inference. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Distillation

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FlexPDA:A Flexible Programming Framework for Deep Learning Accelerators

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Journal of computer Science & Technology 2022年第5期37卷 1200-1220页

作者： Lei Liu Xiu Ma Hua-xiao Liu Guang-li Li Lei Liu College of Computer Science and Technology Jilin UniversityChangchunChina Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina

There are a wide variety of intelligence accelerators with promising performance and energy efficiency,deployed in a broad range of applications such as computer vision and speech ***,programming productivity hinders the deployment of deep learning *** low-level library invoked in the high-level deep learning framework which supports the end-to-end execution with a given model,is designed to reduce the programming burden on the intelligence ***,it is inflexible for developers to build a network model for every deep learning application,which probably brings unnecessary repetitive *** this paper,a flexible and efficient programming framework for deep learning accelerators,FlexPDA,is proposed,which provides more optimization opportunities than the low-level library and realizes quick transplantation of applications to intelligence accelerators for fast *** evaluate FlexPDA by using 10 representative operators selected from deep learning algorithms and an end-to-end *** experimental results validate the effectiveness of FlexPDA,which achieves an end-to-end performance improvement of 1.620x over the low-level library.

关键词： deep learning accelerator programming framework domain-specific language

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DockerSSD: Containerized In-Storage Processing and Hardware Acceleration for Computational SSDs

DockerSSD: Containerized In-Storage Processing and Hardware ...

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IEEE Symposium on High-Performance computer architecture

作者： Donghyun Gouk Miryeong Kwon Hanyeoreum Bae Myoungsoo Jung Computer Architecture and Memory Systems Laboratory KAIST Panmnesia Inc

Processing data in storage is an energy-efficient solution to examine massive datasets. However, a general incarnation of such well-known task-offloading model in a real system is unfortunately unsuccessful due to not only poor performance but also many practical challenges, such as limited processing capabilities and high vulnerabilities at the storage-level. We propose DockerSSD, a fully flexible in-storage processing (ISP) model that can run a variety of applications near flash without their source-level modification. Specifically, it enables lightweight OS-level virtualization in modern SSDs, which allows the storage intelligence to be well harmonized with existing computing environment and makes ISP even faster. Instead of developing a vendor-specific ISP to offload, DockerSSD can reuse existing Docker images, create containers as a self-governing execution object in storage, and process data directly where they are in real-time. To this end, we design a new communication method and virtual firmware that operate together to download Docker images and manage their container execution without a change of the existing storage interface and runtime. We further accelerate ISP and reduce the execution latency by automating container-related network and I/O handling data paths over hardware. Our evaluation shows that DockerSSD is 2.0 × faster than state-of-the-art ISP models for workloads with a high volume of system calls or file accesses. Moreover, it demonstrates a reduction in power and energy consumption by 1.6 × and 2.3 × respectively.

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ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for Rapid Intraoperative Histology 7

ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shi, Lulin Hou, Xingzhong Wong, Ivy H.M. Chan, Simon C.K. Chen, Zhenghui Lo, Claudia T.K. Wong, Terence T.W. Translational and Advanced Bioimaging Laboratory Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Virtual staining has shown great promise in realizing a rapid and low-cost clinical alternative for pathological examinations, eliminating the need for chemical reagents and laborious staining procedures. However, most of the previous studies mainly focus on thin slice samples, which still require tissue sectioning and are unsuitable for intraoperative use. In this paper, we propose a multi-scale model to virtually stain label-free and slide-free biological tissues, allowing hematoxylin- and eosin- (H&E) staining generation in less than a minute for an image with 100 million pixels. We name this ThickV-Stain model, specifically developed to virtually stain intricated and unprocessed thick tissues. We harness the ability of a multi-scale network to encourage the model to capture multiple-level micromorphological characteristics from low-resolution images. Experimental results highlight the advantages of our method for virtual staining on unprocessed thick samples. We also show the effectiveness of ThickV-Stain on thin sections, showing generalizability to other clinical workflows. The proposed method enables us to obtain virtually stained images from unstained samples within minutes and can be seamlessly integrated with downstream pathological analysis tasks, providing an efficient alternative scheme for intraoperative assessment as well as general pathological examination. © 2024 CC-BY 4.0, L. Shi, X. Hou, I.H.M. Wong, S.C.K. Chan, Z. Chen, C.T.K. Lo & T.T.W. Wong.

关键词： Tissue

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Construction of Traditional Culture Ontology Based on Representation and Role 23

Construction of Traditional Culture Ontology Based on Repres...

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2023 4th International Conference on Control, Robotics and Intelligent System, CCRIS 2023

作者： Li, Min Xu, Jianliang Wu, Xiaoquan Ocean University of China Qingdao China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Qingdao China

ISBN: (纸本)9798400708190

Traditional culture refers to a culture that has evolved from civilization and can reflect the characteristics and spirit of a nation. However, at present, the traditional cultural ontology only focuses on modeling and data organization of a certain type of traditional culture, which is a certain distance from the massive cultural information and urgent cultural needs. This article starts from the perspective of protecting and inheriting traditional culture, and based on representation, incorporates role theory to construct a cultural ontology centered on poetry, calligraphy, and painting. The traditional cultural ontology constructed in this article can further contribute to the field of artificial intelligence. © 2023 ACM.

关键词： Ontology

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An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

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computer Modeling in Engineering & Sciences 2023年第1期134卷 637-655页

作者： Zhaoxi Ma Qin Zhao Tianyou Cang Zongjian Li Yiyun Zhu Xinhong Hei Department of Civil Engineering and Architecture Xi’an University of TechnologyXi’an710048China State Key Laboratory of Rail Transit Engineering Informatization(FSDI) Xi’an710043China Department of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China

To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable *** one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction *** cutting waste increases construction costs and generates a considerable amount of CO_(2)*** study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable *** the proposed algorithm,the integer linear programming algorithm was applied to solve the *** was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data *** proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction *** the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation *** the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.

关键词： Building construction rebar work cutting stock problem optimization algorithm integer linear programming

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DUAL-STEP OPTIMIZATION FOR BINARY SEQUENCES WITH HIGH MERIT FACTORS

arXiv

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arXiv 2024年

作者： Pšeničnik, Blaž Mlinarič, Rene Brest, Janez Bošković, Borko Computer Architecture and Languages Laboratory Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia

MSC Codes 68T20The problem of finding aperiodic low auto-correlation binary sequences (LABS) presents a significant computational challenge, particularly as the sequence length increases. Such sequences have important applications in communication engineering, physics, chemistry, and cryptography. This paper introduces a novel dual-step algorithm for long binary sequences with high merit factors. The first step employs a parallel algorithm utilizing skew-symmetry and restriction classes to generate sequence candidates with merit factors above a predefined threshold. The second step uses a priority queue algorithm to refine these candidates further, searching the entire search space unrestrictedly. By combining GPU-based parallel computing and dual-step optimization, our approach has successfully identified new best-known binary sequences for all lengths ranging from 450 to 527, with the exception of length 518, where the previous best-known value was matched with a different sequence. This hybrid method significantly outperforms traditional exhaustive and stochastic search methods, offering an efficient solution for finding long sequences with good merit factors. © 2024, CC BY.

关键词： Stochastic systems

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Accelerating Data Transfer in Dataflow architectures Through a Look-Ahead Acknowledgment Mechanism

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Journal of computer Science & Technology 2022年第4期37卷 942-959页

作者： Yu-Jing Feng De-Jian Li Xu Tan Xiao-Chun Ye Dong-Rui Fan Wen-Ming Li Da Wang Hao Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Beijing Smartchip Microelectronics Technology Company Limited Beijing 100000China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100190China

The dataflow architecture,which is characterized by a lack of a redundant unified control logic,has been shown to have an advantage over the control-flow architecture as it improves the computational performance and power efficiency,especially of applications used in high-performance computing(HPC).Importantly,the high computational efficiency of systems using the dataflow architecture is achieved by allowing program kernels to be activated in a simultaneous ***,a proper acknowledgment mechanism is required to distinguish the data that logically belongs to different *** solutions include the tagged-token matching mechanism in which the data is sent before acknowledgments are received but retried after rejection,or a handshake mechanism in which the data is only sent after acknowledgments are ***,these mechanisms are characterized by both inefficient data transfer and increased area *** performance of the dataflow architecture depends on the efficiency of data *** order to optimize the efficiency of data transfer in existing dataflow architectures with a minimal increase in area and power cost,we propose a Look-Ahead Acknowledgment(LAA)*** accelerates the execution flow by speculatively acknowledging ahead without *** simulation analysis based on a handshake mechanism shows that our LAA increases the average utilization of computational units by 23.9%,with a reduction in the average execution time by 17.4%and an increase in the average power efficiency of dataflow processors by 22.4%.Crucially,our novel approach results in a relatively small increase in the area and power consumption of the on-chip logic of less than 0.9%.In conclusion,the evaluation results suggest that Look-Ahead Acknowledgment is an effective improvement for data transfer in existing dataflow architectures.

关键词： dataflow model control-flow model high-performance computing application data transfer power efficiency

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Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

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International Journal of computers and Applications 2023年第6期45卷 421-432页

作者： Cai, Jing Jiang, Jiawei Wang, Yibing Zheng, Jianwei Xu, Honghui Department of Forensic Science Zhejiang Police College Hangzhou China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting to transform such problems into well-posed ones. However, the low-rank assumption of original visual data is only in an approximate mode, which in turn results in suboptimal recovery of fine-grained details, particularly when the missing rate is extremely high. Moreover, a single prior cannot faithfully capture the complex texture structure of an image. In this paper, we propose a joint usage of Smooth Tucker decomposition and Low-rank Hankel constraint (STLH) for image inpainting, which enables simultaneous capturing of the global low-rankness and local piecewise smoothness. Specifically, based on the Hankelization operation, the original image is mapped to a high-order structure for capturing more spatial and spectral information. By employing Tucker decomposition for optimizing the Hankel tensor and simultaneously applying Discrete Total Variation (DTV) to the Tucker factors, sharper edges are generated and better isotropic properties are enhanced. Moreover, to approximate the essential rank of the Tucker decomposition and avoid facing the uncertainty problem of the upper-rank limit, a reverse strategy is adopted to approximate the true rank of the Tucker decomposition. Finally, the overall image inpainting model is optimized by the well-known alternate least squares (ALS) algorithm. Extensive experiments show that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively. Particularly, in the extreme case with 99% pixels missed, the results from STLH are averagely ahead of others at least 0.9dB in terms of PSNR values. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pixels

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NfvInsight:A Framework for Automatically Deploying and Benchmarking VNF Chains

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Journal of computer Science & Technology 2022年第3期37卷 680-698页

作者： Tian-Ni Xu Hai-Feng Sun Di Zhang Xiao-Ming Zhou Xiu-Feng Sui Sa Wang Qun Huang Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China Peng Cheng Laboratory Shenzhen 518055China Department of Computer Science and Technology Peking UniversityBeijing 100871China

With the advent of virtualization techniques and software-defined networking(SDN),network function virtualization(NFV)shifts network functions(NFs)from hardware implementations to software appliances,between which exists a performance *** to narrow the gap is an essential issue of current NFV ***,the cumbersomeness of deployment,the water pipe effect of virtual network function(VNF)chains,and the complexity of the system software stack together make it tough to figure out the cause of low performance in the NFV *** pinpoint the NFV system performance,we propose NfvInsight,a framework for automatic deployment and benchmarking VNF *** framework tackles the challenges in NFV performance *** framework components include chain graph generation,automatic deployment,and fine granularity *** design and implementation of each component have their *** the best of our knowledge,we make the first attempt to collect rules forming a knowledge base for generating reasonable chain *** deploys the generated chain graphs automatically,which frees the network operators from executing at least 391 lines of bash commands for a single *** diagnose the performance bottleneck,NfvInsight collects metrics from multiple layers of the software ***,we collect the network stack latency distribution ingeniously,introducing only less than 2.2%*** showcase the convenience and usability of NfvInsight in finding bottlenecks for both VNF chains and the underlying *** our framework,we find several design flaws of the network stack,which are unsuitable for packet forwarding inside one single server under the NFV *** optimization for these flaws gains at most 3x performance improvement.

关键词： network function virtualization(NFV) service chain performance bottleneck network stack latency

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