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为提高边缘计算应用场景中数据的传输效率,有效管理并发数据流量,设计一种面向边缘计算的并发数据流接转控制模型。首先,基于数据平面开发套件（DPDK）的绕过内核、多核处理和多网口收发数据包等特点,实现对数据流的并发接收和转发处理;其次,通过建立以模型预测控制（MPC）为核心的系统模型… … 相似文献
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集存储与计算于一身的快速低功耗存内计算架构,突破了存储与计算分离的传统冯·诺依曼体系,解决了限制处理器算力的“内存墙”问题,成为新型计算架构的研究热点. 存内计算的基础器件包括高速且工艺成熟的静态随机存取存储器（static RAM,SRAM）、低功耗高响应且非易失的忆阻器（memristor）、高密度低静态功耗非易失的磁性随机存取存储器（magnetic RAM,MRAM）. 研究者们基于上述器件完成大量存内计算研究,但是关于这些存内计算架构全面且系统总结的文献综述仍然缺失. 首先从SRAM、忆阻器、MRAM方向出发概述了不同器件的存内计算原理、当前存内计算架构发展状况和实际应用场景等. 然后针对当前存内计算架构存在的各种问题和挑战给出了现有解决方案和未来解决方向. 最后对基于以上器件的存内计算研究重点进行了总结并概述了目前的研究短板、展望未来的发展方向.
… … 相似文献9185.
深度神经网络（deep neural network,DNN）在许多现代人工智能（artificial intelligence,AI）任务中取得了最高的精度. 近年来,使用高性能计算平台进行大规模分布式并行训练DNN越来越普遍. 能耗模型在设计和优化DNN大规模并行训练和抑制高性能计算平台过量能耗方面起着至关重要的作用. 目前,大部分的能耗模型都是从设备的角度出发对单个设备或多个设备构成的集群进行能耗建模,由于缺乏从能耗角度对分布式并行DNN应用进行分解剖析,导致罕有针对分布式DNN应用特征进行建模的能耗模型. 针对目前最常用的DNN分布式数据并行训练模式,从DNN模型训练本质特征角度出发,提出了“数据预处理（materials preprocessing）–前向与反向传播（computing）–梯度同步与更新（communicating）”三阶段MC2能耗模型,并通过在国产E级原型机天河三号上使用最多128个MT节点和32个FT节点训练经典的VGG16和ResNet50网络以及最新的Vision Transformer网络验证了模型的有效性和可靠性. 实验结果表明,MC2与真实能耗测量结果相差仅为2.84%,相较4种线性比例能耗模型以及AR,SES,ARIMA时间预测模型准确率分别提升了69.12个百分点,69.50个百分点,34.58个百分点,13.47个百分点,5.23个百分点,22.13个百分点,10.53个百分点. 通过使用的模型可以在超算平台得到DNN模型的各阶段能耗和总体能耗结果,为评估基于能耗感知的DNN大规模分布式数据并行训练及推理各阶段任务调度、作业放置、模型分割、模型裁剪等优化策略的效能提供了基础.
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ObjectiveSkeletal motion retargeting is a key technology that involves adapting skeletal motion data from a source character, after suitable… … 相似文献
《中国图象图形学报》2024,29(12):3712-3726
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ObjectiveIn recent years, with the development of the internet and computer technology, manipulating images and changing their content have become trivial tasks. Therefore, robust image tampering detection methods need to be developed. As passive forensic methods, image forgery methods can be categorized into copy-move, splicing, and inpainting methods. Copy-move involves copying part of the original image to another part of the same image. Many excellent copy-move forgery detection （CMFD） methods have been developed in recent years and can be categorized into block-based, keypoint-based, and deep learning methods. However, these methods have the following drawbacks： 1） they cannot easily detect small or smooth tampered regions; 2） a massive number of features leads to a high computational cost; and 3） false alarm rates are high when the tampered images involve self-similar regions. To solve these issues, a novel CMFD method based on matched pairs, namely, density-based spatial clustering of applications with noise （MP-DBSCAN）, is proposed in this paper along with point density filtering.MethodFirst, a large number of scale-invariant feature transform （SIFT） keypoints are extracted from the input image by lowering the contrast threshold and normalizing the image scale, thus allowing the detection of a sufficient number of keypoints in small and smooth regions. Second, the generalized two nearest neighbor （G2NN） matching strategy is employed to manage multiple keypoint matching, thus allowing the detection algorithm to perform smoothly even when the tampered region has been copied multiple times. A hierarchical matching strategy is then adopted to solve keypoint matching problems involving a massive number of keypoints. To accelerate the matching process, keypoints are initially grouped by their grayscale values, and then the G2NN matching strategy is applied to each group instead of the keypoints detected from the entire image. The efficiency and accuracy of the matching procedure can be improved without deleting the correct matched pairs. Third, an improved clustering algorithm called MP-DBSCAN is proposed. The matched pairs are grouped into different tampered regions, and the direction of the matched pairs are adjusted before the clustering process. The cluster objects only represent one side of the matched pairs and not all the extracted keypoints, and the keypoints from the other side are used as constraints in the clustering process. A satisfying detection result is obtained even when the tampered regions are close to one another. The proposed method obtains better F 1 measures compared with the state-of-the-art copy-move forgery detection methods. Fourth, the prior regions are constructed based on the clustering results. These prior regions can be regarded as the approximate tampered regions. A point density filtering policy is also proposed, where each point density of the region is calculated and the region with the lowest point density is deleted to reduce the false alarm rate. Finally, the tampered regions are located accurately using the affine transforms and the zero-mean normalized cross-correlation （ZNCC） algorithm.ResultThe proposed method is compared with the state-of-the-art CMFD methods on four standard datasets, including FAU, MICC-F600, GRIP, and CASIA v2.0. Provided by Christlein, the FAU dataset has an average resolution of about 3 000 × 2 300 pixels and includes tampered images under post-processing operations （e.g., additional noise and JPEG compression） and various geometrical attacks （e.g., scaling and rotation）. This dataset involves 48, 480, 384, 432, and 240 plain copy-move, scaling, rotation, JPEG, and noise addition operations, respectively. The MICC-F600 dataset includes images in which a region is duplicated at least once. The resolutions of these images range from 800 × 533 to 3 888 × 2 592 pixels. Among the 600 images in this dataset, 440 are original images and 160 are forged images. The GRIP dataset includes 80 original images and 80 tampered images with a low resolution of 1 024 × 768 pixels. Some tampered regions on these images are very smooth or small. The size of the tampered regions ranges from about 4 000 to 50 000 pixels. The CASIA v2.0 dataset contains 7 491 authentic and 5 123 forged images, of which 1 313 images are forged using copy-move methods. Precision, recall, and F 1 scores are used as assessment criteria in the experiments. The F 1 scores of the proposed method on the FAU, MICC-F600, GRIP, and CASIA v2.0 datasets at the pixel level are 0.914 3, 0.890 6, 0.939 1, and 0.856 8, respectively. Extensive experimental results demonstrate the superior performance of the proposed method compared with the existing state-of-the-art methods. The effectiveness of the MP-DBSCAN algorithm and the point density filtering policy is also demonstrated via ablation studies.ConclusionTo detect tampered regions accurately, a novel CMFD method based on the MP-DBSCAN algorithm and the point density filtering policy is proposed in this paper. The matched pairs of an image can be divided into different tampered regions by using the MP-DBSCAN algorithm to detect these regions accurately. The mismatched pairs are then discarded by the point density filtering policy to reduce false alarm rates. Extensive experimental results demonstrate that the proposed method exhibits a satisfactory accuracy and robustness compared with the existing state-of-the-art methods.… … 相似文献
《中国图象图形学报》2024,29(12):3595-3611
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区块链不可篡改的特性保证了链上数据的一致性,而链下数据可能在记录、存储、传输的过程中遭到破坏,这会造成链上和链下数据不一致的问题,进而极大地影响区块链技术的落地发展;因此,需要一些机制保证链上链下数据的一致性。针对链上和链下数据不一致的问题,总结目前的一些链上链下一致性保护技术。… … 相似文献
高婷婷;姚中原;贾淼;斯雪明 《计算机应用》2024,(12):3658-3668
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视频异常行为检测作为计算机视觉的研究热点;通过提取视频内容时间和空间特征;判断视频中是否存在异常事件和事件种类;定位异常发生的区域和时间。以有监督/无监督学习为线索;对现有视频异常行为检测方法进行系统梳理和归纳。在有监督类方法中;细分为基于偏差均值计算方法和基于多模态方法;在无监… … 相似文献
《计算机科学与探索》2024,18(12)
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在动态优化问题（DOP）中环境的变化可描述为不同类型的动态;动态优化算法（DOA）对环境的适应性十分重要。此外;DOA的局部和全局多样性损失是导致其开发和勘探能力下降的主因之一。在动态环境中保持局部和全局多样性可有效避免多样性损失。为此;提出一种基于搜索引导网络的粒子群优化算法（… … 相似文献
《计算机科学与探索》2024,18(12)
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网站版权所有：中国科学院软件研究所 京ICP备09084417号-9
地址：北京海淀区中关村南四大街4号 邮编：100190 电话：010-62562563 E-mail ： cj_mail@jscas.ac.cn
本系统由:北京勤云科技发展有限公司提供技术支持
