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A hybrid memory architecture supporting fine-grained data migration

Frontiers of computer science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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Energy- and Area-Efficient CMOS Neuron and Max Pooling Circuit for RRAM-Based CNN Accelerators

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IEEE Access 2025年 13卷 84329-84340页

作者： Kim, Dowon Lee, Byung-Geun Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of

This study proposes an energy- and area-efficient CMOS neuron and analog max pooling circuit for RRAM-based convolutional neural network (CNN) accelerators. The proposed max pooling circuit implements a 2 × 2 max pooling operation with a simple analog circuit in the current domain. The currentmode max pooling is performed before the current-to-voltage and voltage-to-digital conversions, thus reducing the number of circuits required for the conversions in the conventional CNN accelerators. Consequently, the 2 × 2 pooling circuit reduces the energy and area of the accelerator to a quarter. In addition to the max pooling circuit, the neuron circuit improves energy efficiency. The proposed neuron circuit implements the ReLU activation function along with digital conversion by modifying the operating principle of a conventional single-slope analog-to-digital converter. By implementing the ReLU function and digital conversion, the neuron achieves high energy efficiency by applying a high-speed asynchronous operation. A prototype chip, which included the neuron and current-mode max pooling circuit, was fabricated using a 130 nm standard CMOS process. The measurement results of the chip demonstrate its energy efficiency of 5.5 TOPS/W. Additionally, a simulation for image recognition demonstrates that a CNN accelerator employing the proposed neuron and max pooling circuit achieves 96.2 and 82.7% classification accuracies for the MNIST and Fashion-MNIST datasets, respectively. © 2013 IEEE.

关键词： RRAM

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Rotation,Translation and Scale Invariant Sign Word Recognition Using Deep Learning

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computer Systems science & engineering 2023年第3期44卷 2521-2536页

作者： Abu Saleh Musa Miah Jungpil Shin Md.Al Mehedi Hasan Md Abdur Rahim Yuichi Okuyama School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science and Engineering Pabna University of Science and TechnologyPabnaBangladesh

Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious *** of the main functions of sign language is to communicate with each other through hand *** of hand gestures has become an important challenge for the recognition of sign *** are many existing models that can produce a good accuracy,but if the model test with rotated or translated images,they may face some difficulties to make good performance *** resolve these challenges of hand gesture recognition,we proposed a Rotation,Translation and Scale-invariant sign word recognition system using a convolu-tional neural network(CNN).We have followed three steps in our work:rotated,translated and scaled(RTS)version dataset generation,gesture segmentation,and sign word ***,we have enlarged a benchmark dataset of 20 sign words by making different amounts of Rotation,Translation and Scale of the ori-ginal images to create the RTS version *** we have applied the gesture segmentation *** segmentation consists of three levels,i)Otsu Thresholding with YCbCr,ii)Morphological analysis:dilation through opening morphology and iii)Watershed ***,our designed CNN model has been trained to classify the hand gesture as well as the sign *** model has been evaluated using the twenty sign word dataset,five sign word dataset and the RTS version of these *** achieved 99.30%accuracy from the twenty sign word dataset evaluation,99.10%accuracy from the RTS version of the twenty sign word evolution,100%accuracy from thefive sign word dataset evaluation,and 98.00%accuracy from the RTS versionfive sign word dataset ***,the influence of our model exists in competitive results with state-of-the-art methods in sign word recognition.

关键词： Sign word recognition convolution neural network(cnn) rotation translation and scaling(rts) otsu segmentation

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Introduction to the Special Issue on the Bottleneck of Blockchain Techniques Scalability,Security and Privacy Protection

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computer Modeling in engineering & sciences 2024年第12期141卷 1933-1937页

作者： Shen Su Daojing He Neeraj Kumar Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen518055China Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004India

Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising *** the success of existing blockchain architectures like Bitcoin,Ethereum,Filecoin,Hyperledger Fabric,BCOS,and BCS,current blockchain applications are still quite *** struggles with scenarios requiring high-speed transactions(e.g.,online markets)or large data storage(e.g.,video services)due to consensus efficiency *** restrictions pose risks to investors in blockchain-based economic systems(e.g.,DeFi),deterring current and potential *** protection challenges make it difficult to involve sensitive data in blockchain applications.

关键词： services Ether limitations

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CBIR: a novel identification approach for college students in need based on consumer behavior psychology theory

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Neural Computing and Applications 2025年第6期37卷 4663-4677页

作者： Liu, Xinze Liu, Shixi Hu, Xiaojing Zhang, Yudong Fang, Xianwen School of Computer and Information Engineering Chuzhou University Chuzhou239000 China School of Computer Science and Engineering Southeast University Nanjing210096 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

The accurate identification of students in need is crucial for governments and colleges to allocate resources more effectively and enhance social equity and educational fairness. Existing approaches to identifying students in need rely on manual operations that include manually extracting consumption behavior information, statistical consumption characteristics and principal component analysis. However, this issue may lead to low prediction accuracy and inefficiency in identifying students in need. We design a three-stage framework to accurately identify college students in need from the perspective of consumer behavior psychology. The consumption behavior information is first obtained from the student consumption records using the consumption behavior clustering approach. The consumption behavior matrix is then built by extracting consumption and spatiotemporal information in different periods. Finally, a novel consumption behavior identification ResNeSt (CBIR) model is proposed to identify college students in need accurately. The experimental results on real datasets show that the CBIR model has higher prediction accuracy than the baseline models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Social psychology

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An End-to-End Two-Branch Network Towards Robust Video Fingerprinting

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2371-2384页

作者： Xu, Yingying Zhou, Yuanding Li, Xinran Zhao, Gejian Qin, Chuan University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

With the increasing number of edited videos, many robust video fingerprinting schemes have been proposed to solve the problem of video content authentication. However, most of them either deal with the temporal and spatial features symmetrically or insufficiently consider the temporal information. In this work, an end-to-end two-branch network toward robust video fingerprinting (RVFNet) is proposed, where the two branches focus on the temporal and spatial information, respectively. The temporal branch aims to comprehensively capture complex motion patterns by combining subtle motion changes with the overall motion trend. The spatial branch exploits the pixel-level information obtained by multiple receptive fields while preserving significant structural features. Deep metric learning is employed in the training process, and we adopt hard triplet loss to constrain the generation of fingerprints. Furthermore, we construct a large-scale and complex dataset for the robust video fingerprinting task based on multiple video content-preserving manipulations in actual scenarios. The size of our dataset exceeds most datasets adopted in the current robust video fingerprinting schemes. Based on the proposed dataset, experimental results demonstrate that our scheme achieves outstanding performance improvements compared with the state of the art. © 2020 IEEE.

关键词： Convolution

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FIMKD: Feature-Implicit Mapping Knowledge Distillation for RGB-D Indoor Scene Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6488-6499页

作者： Zhou, Wujie Xiao, Yuxiang Liu, Yuanyuan Jiang, Qiuping Zhejiang University of Science & Technology School of Information & Electronic Engineering Hangzhou310023 China Nanyang Technological University School of Computer Science and Engineering Singapore308232 Singapore China University of Geosciences School of Computer and Technology Wuhan430074 China Ningbo University School of Information Science and Engineering Ningbo315211 China

Depth images are often used to improve the geometric understanding of scenes owing to their intuitive distance properties. Although there have been significant advancements in semantic segmentation tasks using red-green-blue-depth (RGB-D) images, the complexity of existing methods remains high. Furthermore, the requirement for high-quality depth images increases the model inference time, which limits the practicality of these methods. To address this issue, we propose a feature-implicit mapping knowledge distillation (FIMKD) method and a cross-modal knowledge distillation (KD) architecture to leverage deep modal information for training and reduce the model dependence on this information during inference. The approach comprises two networks: FIMKD-T, a teacher network that uses RGB-D data, and FIMKD-S, a student network that uses only RGB data. FIMKD-T extracts high-frequency information using the depth modality and compensates for the loss of RGB details due to a reduction in resolution during feature extraction by the high-frequency feature enhancement module, thereby enhancing the geometric perception of semantic features. In contrast, the FIMKD-S network does not employ deep learning techniques;instead, it uses a nonlearning approach to extract high-frequency information. To enable the FIMKD-S network to learn deep features, we propose a feature-implicit mapping KD for feature distillation. This mapping technique maps the features in channel and space to a low-dimensional hidden layer, which helps to avoid inefficient single-pattern student learning. We evaluated the proposed FIMKD-S∗ (FIMKD-S with KD) on the NYUv2 and SUN-RGBD datasets. The results demonstrate that both FIMKD-T and FIMKD-S∗ achieve state-of-the-art performance. Furthermore, FIMKD-S∗ provides the best performance balance. © 2020 IEEE.

关键词： Semantic Segmentation

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Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

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computer Modeling in engineering & sciences 2023年第9期136卷 2425-2450页

作者： Mengkai Zhao Zhixia Zhang Tian Fan Wanwan Guo Zhihua Cui The School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China The School of Electronic Information Engineering Taiyuan University of Science and TechnologyTaiyuan030024China

Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud *** a reasonable resource allocation solution is the key to adequately utilize the hybrid ***,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other *** on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion ***,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model *** algorithm uses opposition-based learning to generate initial populations for faster ***,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search *** comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.

关键词： Hybrid cloud environment task scheduling many-objective optimization model many-objective optimization algorithm

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Unsupervised anomaly detection and localization via bidirectional knowledge distillation

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Neural Computing and Applications 2024年第29期36卷 18499-18514页

作者： Wang, Xiaoming Wang, Yongxiong Pan, Zhiqun Wang, Guangpeng School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Knowledge distillation has demonstrated significant potential in addressing the challenge of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher–student (T-S) model provides evidence for anomaly detection and localization. However, the teacher model is pretrained for classification, while the anomaly scores in the distillation-based anomaly detection method are indirectly derived from the classification scores. The mismatch between the two tasks can hinder the optimization of the model. To tackle this issue, we propose an innovative bidirectional knowledge distillation model. In this approach, forward knowledge distillation is pivotal in bolstering the model’s capacity for generalization. Simultaneously, backward knowledge distillation promotes diversity in representing anomalies. This reciprocal knowledge exchange effectively wards off potential performance declines due to target inconsistency. Through bidirectional knowledge distillation, we establish a more encompassing and resilient framework for knowledge transfer. Additionally, we introduce a novel data augmentation strategy to simulate anomalies and effectively eliminate unnecessary noise. In experiments on the MVTec AD, the proposed model achieves competitive results compared to state-of-the-art methods, 97.47% on image-level AUC, 98.23% on pixel-level AUC, and 94.77% on instance-level PRO. These results demonstrate the practicality of our approach in anomaly detection and localization. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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