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Accelerating BERT inference with GPU-efficient exit prediction

Frontiers of Computer science 2024年第3期18卷 31-42页

作者： Lei LI Chengyu WANG Minghui QIU Cen CHEN Ming GAO Aoying ZHOU Shanghai Engineering Research Center of Big Data Management School of Data Science and EngineeringEast China Normal UniversityShanghai 200062China Alibaba Group Hangzhou 311121China KLATASDS-MOE School of StatisticsEast China Normal UniversityShanghai 200062China

BERT is a representative pre-trained language model that has drawn extensive attention for significant improvements in downstream Natural Language Processing(NLP)*** complex architecture and massive parameters bring BERT competitive performance but also result in slow speed at model inference *** speed up BERT inference,FastBERT realizes adaptive inference with an acceptable drop in accuracy based on knowledge distillation and the early-exit ***,many factors may limit the performance of FastBERT,such as the teacher classifier that is not knowledgeable enough,the batch size shrinkage and the redundant computation of student *** overcome these limitations,we propose a new BERT inference method with GPU-Efficient Exit Prediction(GEEP).GEEP leverages the shared exit loss to simplify the training process of FastBERT from two steps into only one step and makes the teacher classifier more knowledgeable by feeding diverse Transformer outputs to the teacher *** addition,the exit layer prediction technique is proposed to utilize a GPU hash table to handle the token-level exit layer distribution and to sort test samples by predicted exit *** this way,GEEP can avoid batch size shrinkage and redundant computation of student *** results on twelve public English and Chinese NLP datasets prove the effectiveness of the proposed *** source codes of GEEP will be released to the public upon paper acceptance.

关键词： BERT FastBERT inference acceleration model distillation early exit text classification

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Robust and Trustworthy data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

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Computers, Materials & Continua 2024年第2期78卷 2159-2179页

作者： Jinyang Yu Xiao Zhang Jinjiang Wang Yuchen Zhang Yulong Shi Linxuan Su Leijie Zeng School of Computer Science Northwestern Polytechnical UniversityXi’an710072China MIIT Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityXi’an710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi’an710072China

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy *** distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high *** address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage *** architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and *** on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw *** off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data *** provide a unified access interface for user-friendly system *** testing validates the system’s reliability and stable *** proposed approach significantly enhances storage capacity compared to standalone blockchain *** reliability tests consistently yield positive *** average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

关键词： On-chain and off-chain collaboration blockchain distributed storage system hyperledger fabric IPFS cluster

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Attribute-wise Sample Generator: A Module for Generalized Long Tail data

Attribute-wise Sample Generator: A Module for Generalized Lo...

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Yu, Daao Gu, Pengfei School of Big Data University of Science and Technology of China Hefei China

ISBN: (纸本)9781510671881

Existing long-tail classification (LT) methods ignore attribute class imbalance and focus only on addressing class imbalance where the head class has more samples than the tail class. In fact, even if the classes are balanced, the samples in each class can still be long-tailed due to attribute variability. The latter is fundamentally more general and difficult than the former, not only because attributes are implicitly included in most datasets, but also because they are more complex to construct. Therefore, we introduce a new research question, generalized long-tail classification [1] (GLT), to jointly consider these two imbalances. And the ASG model is proposed to deal with the long-tail problem of attributes on the data set. ASG is a generalized class rebalancing method, which aims to balance the classes and attributes of the training data. It uses a feature displacement model to extract feature displacements, and then generates new samples according to the distribution of attributes in each class. Experiments prove that ASG has achieved good results on various datasets. © 2023 SPIE. All rights reserved.

关键词： Classification (of information)

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology 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 Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

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Towards Optimization and Assessment of the Chinese New-generation VLBI Network for Earth Orientation Parameters Monitoring

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Research in Astronomy and Astrophysics 2025年第2期25卷 256-268页

作者： Hao Xiang Zhong-Kai Zhang Lan Du Ze-Jun Liu Xu-Qiao Wang College of Geospatial Information Information Engineering University Henan Industrial Technology Academy of Spatio-Temporal Big Data College of Geography and Environmental Science Henan University

The Earth Orientation Parameters(EOP) provide a time-varying transition relationship between the International Terrestrial Reference Frame and the International Celestial Reference Frame. To support deep space exploration and the Beidou Navigation Satellite System, the Chinese New-generation Very Long Baseline Interferometry Network(CNVN) is under construction for independent monitoring of the EOP. This paper evaluates the performance of existing 4-antenna CNVN through a batch generated observation schedules followed by extensive Monte Carlo simulations. The optimal positions of the fifth and sixth antennas of CNVN are found from 24hypothetical antenna positions uniformly distributed in China. In this process, the weighted parameters are optimized, which not only reduce the possibility of large error of EOP estimation accuracy due to unreasonable combination, but also greatly reduce the calculation cost.

关键词： methods: analytical techniques: interferometric reference systems

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense Network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

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Computer Modeling in Engineering & sciences 2024年第6期139卷 3209-3231页

作者： Weiya Shi Shaowen Zhang Shiqiang Zhang College of Artificial Intelligence and Big Data Henan University of TechnologyZhengzhou450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou450001China

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural *** these efforts,the detection of small objects in remote sensing remains a formidable *** deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep ***,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection ***,the sensitivity of small objects to the bounding box perturbation further increases the detection *** this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote *** address feature loss in deep layers,we have devised a cross-layer attention fusion *** noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid ***,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)*** efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available *** experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

关键词： Small object detection attention mechanism cross-layer fusion discrete cosine transform

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A verifiable essential secret image sharing scheme based on HLRs(VESIS-(t,s,k,n))

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CAAI Transactions on Intelligence technology 2024年第2期9卷 388-410页

作者： Massoud Hadian Dehkordi Seyed Taghi Farahi Samaneh Mashhadi Cryptography and Data Security Laboratory School of MathematicsIran University of Science and TechnologyNarmakTehranIran

In traditional secret image sharing schemes,a secret image is shared among shareholders who have the same *** if the shareholders have two different positions,essential and non‐essential,it is necessary to use essential secret image sharing *** this article,a verifiable essential secret image sharing scheme based on HLRs is ***'s share consists of two *** first part is produced by the shareholders,which prevents the fraud of *** second part is a shadow image that is produced by using HLRs and the first part of *** verification of the first part of the shares is done for the first time by using multilinear and bilinear ***,for verifying shadow images,Bloom Filters are used for the first *** proposed scheme is more efficient than similar schemes,and for the first part of the shares,has formal security.

关键词： Bloom Filter cryptography Essential Secret Image Sharing Verifiability

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Integer wavelet transform-based secret image sharing using rook polynomial and hamming code with authentication

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1435-1450页

作者： Sara Charoghchi Zahra Saeidi Samaneh Mashhadi Cryptography and Data Security Laboratory School of Mathematics and Computer ScienceIran University of Science&TechnologyTehranIran

As an effective way to securely transfer secret images,secret image sharing(SIS)has been a noteworthy area of *** in a SIS scheme,a secret image is shared via shadows and could be reconstructed by having the required number of them.A major downside of this method is its noise-like shadows,which draw the malicious users'*** order to overcome this problem,SIS schemes with meaningful shadows are introduced in which the shadows are first hidden in innocent-looking cover images and then *** most of these schemes,the cover image cannot be recovered without distortion,which makes them useless in case of utilising critical cover images such as military or medical ***,embedding the secret data in Least significant bits of the cover image,in many of these schemes,makes them very fragile to steganlysis.A reversible IWT-based SIS scheme using Rook polynomial and Hamming code with authentication is *** order to make the scheme robust to steganalysis,the shadow image is embedded in coefficients of Integer wavelet transform of the cover *** Rook polynomial makes the scheme more secure and moreover makes authentication very easy and with no need to share private key to ***,utilising Hamming code lets us embed data with much less required modifications on the cover image which results in high-quality stego images.

关键词： hamming code homogeneous linear recursion IWT rook polynomial (t n)-secret image sharing

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Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network

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Computers, Materials & Continua 2024年第4期79卷 1659-1677页

作者： Zhanfeng Wang Lisha Yao School of Computer Science and Artificial Intelligence Chaohu UniversityHefei238000China School of Big Data and Artificial Intelligence Anhui Xinhua UniversityHefei230088China

Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy.

关键词： Expression recognition capsule neural network convolutional neural network

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