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Using Transfer Learning in Building Federated Learning Models on Edge Devices 2

Using Transfer Learning in Building Federated Learning Model...

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2nd international conference on Intelligent Data Science Technologies and applications (IDSTA)

作者： Suzuki, Jordan Lameh, Saba F. Amannejad, Yasaman Mt Royal Univ Dept Math & Comp Calgary AB Canada Univ Tabriz Dept Comp Engn Tabriz Iran

ISBN: (纸本)9781665421805

Today's state-of-the-art machine learning techniques, such as deep networks, are typically trained using cloud platforms, leveraging the elastic scalability of the cloud. For such processing, data from various sources need to be transferred to a cloud server. While this works well for some application domains, it is not suitable for all due to privacy and network overhead concerns. Federated learning is a machine learning setting designed for learning from distributed data over edge devices. In federated learning, data does not leave users' devices. Instead, each user trains a local model with their data and shares the model updates with a federated server to aggregate and build a global model. Such training respects users' privacy while reducing the network overhead for transferring data to a central server. Despite all advantages, each user usually has limited data that might not be enough to train a well-performing local model. In this work, to solve this challenge while keeping the client data private, we combine transfer learning with federated learning, where we train a base model with a public dataset. The base model is passed to the federated users to be fine-tuned for the target task. We have built a federated image classification model for the CIFAR-100 dataset using transfer learning and have studied how different parameters affect the model accuracy. Using this process, we achieve a model accuracy similar to a model trained in a central fashion while keeping the data private.

关键词： distributed Machine Learning Federated Learning Transfer Learning Edge Computing

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Efficient Large Scale Reverse-time Migration Imaging Computation based on distributed Spark Cluster with GPUs

Efficient Large Scale Reverse-time Migration Imaging Computa...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Suhui Wangzhang Ruizhang Yang Rong Gu Bo Li Dingjin Liu Zhaokang Wang State Key Laboratory for Novel Software Technology Nanjing University China Sinopec Geophysical Research Institute China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

The reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast development of seismic data acquisition technology, there exists large amount of seismic data. Thus, the requirement for the efficient computation of the reverse-time migration (RTM) imaging algorithms over large-scale data have been increased greatly. To address this problem, in this paper, we propose a distributed reverse-time migration imaging algorithm based on the widely-used Spark platform first. Based on that, we design and implement a RTM computation solution based on Spark and GPU with several optimizations, including pre-shuffle&caching and load balancing. We also analyze the fault tolerance capability of the proposed distributed algorithm. We evaluate the proposed methods on the typical three-dimensional wavefild benchmark dataset. Experimental results show that the performance of the Spark based reverse-time migration imaging algorithm can scale up near linearly with computing nodes. In addition, the GPU+Spark based version has significant performance improvement on execution efficiency over the Spark-based algorithm. Moreover, the proposed optimization strategies also take effect. In addition, the method proposed in this paper has been applied in the real-world environment of one of the top oil exploration companies world wide, and has achieved significant improvement under the premise of the algorithm correctness guarantee.

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Virtualization techniques for Secure Cloud Architectures: An Analysis of Risks and Countermeasures

Virtualization Techniques for Secure Cloud Architectures: An...

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Decision Aid Sciences and Application (DASA), international conference on

作者： S. R. Deshmukh Prasad Parjane Gokul Raktate Sushant More Rohit Wakchaure Shreyash Pangavhane Department of Computer Engineering Sanjivani College Of Engineering Kopargaon India

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This is a fascinating perspective that is simplifying the way consumers engage with computing and related functions. The structure of the cloud is not something that one may describe as a start; however, it was working on a different technology that was based on the many services that it offered. It also has application software for the users to access for such tasks as word processing. Additionally, cloud computing allows customers to take advantage of cloud computing infrastructure by designing and deploying their applications on the cloud. It is capable of providing resilient distributed file system storage designed for storing data that usually gets dispersed across various geographic regions. Virtualization technology is one of the core offerings provided when one is using the cloud. The concept is the ability of physical servers to host multiple virtual machines. This has worked well and has been of great benefit to the users while on the cloud. In this essay, a review of the evidence from the existing research literature on cloud virtualization is provided. This investigation was performed through a literature review of some related works on cloud virtualization. Some of the literature reviews done were undertaken in a credible manner. This paper identifies the present conditions of cloud virtualization trends and assists in the directions of subsequent studies. The main focus of this work is to pose the following question: what is new and how is the cloud virtualization developing? The reviewed works include journal publications and conferences as well as white papers and magazines that have published good works. Therefore, at the end of this review, the trends in cloud virtualization as discussed should have been able to come out. This will be of help to the potential user and even the cloud providers.

关键词： Cloud computing Virtual machine monitors Scalability Side-channel attacks Market research Virtual machines Security Servers Systematic literature review Text processing

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Deep-to-Bottom Weights Decay: A Systemic Knowledge Review Learning Technique for Transformer Layers in Knowledge Distillation 1

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15th international conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Wang, Ankun Liu, Feng Huang, Zhen Hu, Minghao Li, Dongsheng Chen, Yifan Xie, Xinjia Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China Informat Res Ctr Mil Sci Beijing Peoples R China

ISBN: (数字)9783031109867

ISBN: (纸本)9783031109867;9783031109850

There are millions of parameters and huge computational power consumption behind the outstanding performance of pre-trained language models in natural language processing tasks. Knowledge distillation is considered as a compression strategy to address this problem. However, previous works have the following shortcomings: (i) distill partial transformer layers of the teacher model, which not only do not make full use of the teacher-side information. But also break the coherence of the information, (ii) neglect the difficulty differences of knowledge from deep to shallow, which corresponds to different level information of teacher model. In this paper, we introduce a deep-to-bottom weights decay review mechanism to knowledge distillation, which could fuse teacher-side information while taking each layer's difficulty level into consideration. To validate our claims, we distill a 12-layer BERT into a 6-layer model and evaluate it on the GLUE dataset. Experimental results show that our review approach is not only able to outperform other existing techniques, but also outperform the original model on partial datasets.

关键词： Knowledge distillation Pre-training language model Model compression

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for Highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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A One-for-All and O(V log(V))-Cost Solution for parallel Merge Style Operations on Sorted Key-Value Arrays 27

A One-for-All and O(V log(V))-Cost Solution for Parallel Mer...

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27th ACM international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Wang, Bangyan Deng, Lei Sun, Fei Dai, Guohao Liu, Liu Wang, Yu Xie, Yuan Univ Calif Santa Barbara Santa Barbara CA 93106 USA Tsinghua Univ Beijing Peoples R China Alibaba DAMO Acad Hangzhou Peoples R China

ISBN: (纸本)9781450392051

The processing of sorted key-value arrays using a "merge style operation (MSO)" is a very basic and important problem in domains like scientific computing, deep learning, database, graph analysis, sorting, set-operation etc. MSOs dominate the execution time in some important applications like SpGEMM and graph mining. For example, sparse vector addition as an MSO takes up to 98% execution time in SpGEMM in our experiment. For this reason, accelerating MSOs on CPU, GPU, and accelerators using parallel execution has been extensively studied but the solutions in prior work have three major limitations. (1) They treat different MSOs as isolated problems using incompatible methods and an unified solution is still lacking. (2) They do not have the flexibility to support variable key/value sizes and value calculations in the runtime given a fixed hardware design. (3) They require a quadratic hardware cost (O(V-2)) for given parallelism V in most cases. To address above three limitations, we make the following efforts. (1) We present a one-for-all solution to support all interested MSOs based on a unified abstraction model "restricted zip machine (RZM)". (2) We propose a set of composable and parallel primitives for RZM to provide the flexibility to support variable key/value sizes and value calculations. (3) We provide the hardware design to implement the proposed primitives using only O(V log(V)) resource. With the above techniques, a flexible and efficient solution for MSOs has been built. Our design can be used either as a drop-in replacement of the merge unit in prior accelerators to reduce the cost from O(V-2) to O(V log(V)), or as an extension to the SIMD ISA of CPU and GPU. In our evaluation on CPU, when V = 16 (512-bit SIMD, 32-bit element), we achieve significant speedup on a range of representative kernels including set operations (8.4x), database joins (7.3x), sparse vector/matrix/tensor addition/multiplication on real/complex numbers (6.5x), merge sort (8.0x ove

关键词： SIMD Key-value array Sparse linear algebra SpGEMM Merge sort Graph Join

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StencilMART: Predicting Optimization Selection for Stencil Computations across GPUs

StencilMART: Predicting Optimization Selection for Stencil C...

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international Symposium on parallel and distributed processing (IPDPS)

作者： Qingxiao Sun Yi Liu Hailong Yang Zhonghui Jiang Zhongzhi Luan Depei Qian State Key Laboratory of Software Development Environment Beijing China School of Computer Science and Engineering Beihang University Beijing China

Stencil computations are widely used in high performance computing (HPC) applications. Many HPC platforms utilize the high computation capability of GPUs to accelerate stencil computations. In recent years, stencils have become more diverse in terms of stencil order, memory accesses and computation patterns. To adapt diverse stencils to GPUs, a variety of optimization techniques have been proposed such as streaming and retiming. However, due to the diversity of stencil patterns and GPU architectures, no single optimization technique fits all stencils. Besides, it is challenging to choose the most cost-efficient GPU for accelerating target stencils. To address the above problems, we propose StencilMART, an automatic optimization selection framework that predicts the best optimization combination and execution time under a certain parameter setting for stencils on GPUs. Specifically, the StencilMART represents the stencil patterns as binary tensors and neighboring features through tensor assignment and feature extraction. In addition, the StencilMART implements various machine learning methods such as classification and regression that utilize stencil representation and hardware characteristics for execution time prediction. The experiment results show that the StencilMART can achieve accurate optimization selection and performance prediction for various stencils across GPUs.

关键词： distributed processing Tensors High performance computing Graphics processing units Machine learning Computer architecture Feature extraction

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Boosting DNN Efficiency: Replacing FC Layers with Graph Embeddings for Hardware Acceleration

Boosting DNN Efficiency: Replacing FC Layers with Graph Embe...

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Digital Signal processing and its applications (DSPA), international conference on

作者： Vasiliy S. Usatyuk Sergey I. Egorov Department of Computer Science South-West State University Kursk Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Fully connected (FC) layers present a significant bottleneck in the throughput of Convolutional Neural Networks (CNNs) and Transformers when deployed on hardware accelerators such as FPGAs, ASICs, and analog AI chips. This paper proposes a novel approach to mitigate this limitation by replacing FC layers with sparse graph embedding techniques derived from Low-Density Parity-Check (LDPC) codes, enabling robust weight compression. Using VGG16 as a case study, we demonstrate that our method reduces the parameters in the FC layers from 123,642,856 to just 64,000-a 1,931-fold reduction-while maintaining high-precision classification performance. Following aggressive pruning (72% weight reduction), our LDPC-based embeddings, combined with 8-bit fixed-point quantization, achieve an accuracy of 70.21%, compared to 70.89% achieved by unpruned float32. On the Xilinx Alveo U50 platform, this translates to processing up to 1119 images per second with parallel computation of 64-dimensional embeddings. Moreover, by replacing the top FC layers with the proposed graph embedding method and leveraging fast analog feature extraction (via supervised or random projections), our approach demonstrates the potential to achieve peak performance on the Alveo U50 of 4.8 million images per second for float32 100-class classification and 39.86 million images per second for 10-class classification using 16-bit fixed-point representation. These findings underscore the capability of graph embeddings to significantly enhance DNN efficiency on data center and resource-constrained edge AI hardware platforms. We hypothesize that exploiting the inherent sparsity of graph embeddings, combined with the energy efficiency of analog computation for feature extraction, can yield substantial reductions in CNN, Transformer and Diffusion DNN inference power consumption, potentially surpassing GPU efficiency by three to four orders of magnitude. This paper presents preliminary results and outlines directions for

关键词： Accuracy Digital signal processing Transformers Feature extraction Throughput Parity check codes Energy efficiency Convolutional neural networks Hardware acceleration Field programmable gate arrays

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Big data collaborative artificial intelligence and high-performance computing to drive physical design of fusion

Big data collaborative artificial intelligence and high-perf...

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2022 international conference on Cloud Computing, Big Data applications and Software Engineering, CBASE 2022

作者： Liu, Pan Liu, Gongjie Liu, Jie Liu, Chencheng Xiong, Wenbin Tang, Zhangcun Zhou, Yinzhao Wang, Yonggang Yuan, Zhouming Beijng Paratera Technology Corp. Ltd Beijing100000 China National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha410073 China National University of Defense Technology Laboratory of Software Engineering for Complex Systems Changsha410073 China Central South University of Forestry and Technology Changsha410000 China Nuclear and Radiation Safety Center Mee Beijing100000 China University of Electronic Science and Technology of China Chengdu610000 China Tianfu Innovation Energy Establishment Chengdu610000 China

ISBN: (纸本)9798350396393

Controlled thermonuclear fusion has always been a dream pursued by mankind. However, the physical processes of controlled thermonuclear fusion are complex, requiring numerical simulations with high performance computing, and the amount of data generated by the physical processes on spatial, temporal and temperature scales is too large to be captured, managed, processed and collated in a reasonable time frame by mainstream software tools to achieve more aggressive fusion physical design. The data are too large to be captured, managed, processed, and collated into more aggressive targets for fusion physical design in a reasonable time by mainstream software tools. At the same time, the failure of fusion ignition can be caused by the distortion of various key physical quantities, and only by decomposing the process step by step and clarifying the changes of key physical quantities in the fusion physics process, can an effective mechanism be formed to prevent the distortion of key physical quantities from causing ignition failure in experimental physics. Big data in collaboration with artificial intelligence and high performance computing to drive the physical design of fusion is a novel avenue. By data acquisition with and pre-processing, this allows the creation of small sample libraries and deep learning. With supervised learning function convergence, incorporating solid/fluid computational methods, further network layering and cell expansion, the parameters of the physical model conforming to Lawson's criterion will become experimental physical parameters, and we find the new approach in Data capacity, Model combination approach, Type of material, Calculation speed, Optimisation of design iteration times, etc. are superior to the traditional approach. © 2022 IEEE.

关键词： Big data

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Hybrid Multi-Modal Recommendation System: Integrating MMOE and XGBoost for Enhanced Personalization and Accuracy

Hybrid Multi-Modal Recommendation System: Integrating MMOE a...

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Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE international conference on

作者： Yang Wang Kowei Shih AI Scientist Toronto Canada Tsinghua University Beijing China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

In the field of computer vision, 6D object detection and pose estimation are critical for applications such as robotics, augmented reality, and autonomous driving. Traditional methods often struggle with achieving high accuracy in both object detection and precise pose estimation simultaneously. This study proposes an improved 6D object detection and pose estimation pipeline based on the existing 6D-VNet framework, enhanced by integrating a Hybrid Task Cascade (HTC) and a High-Resolution Network (HRNet) backbone. By leveraging the strengths of HTC's multi-stage refinement process and HRNet's ability to maintain high-resolution representations, our approach significantly improves detection accuracy and pose estimation precision. Furthermore, we introduce advanced post-processing techniques and a novel model integration strategy that collectively contribute to superior performance on public and private benchmarks. Our method demonstrates substantial improvements over state-of-the-art models, making it a valuable contribution to the domain of 6D object detection and pose estimation.

关键词： Training Accuracy Reviews Scalability Pose estimation distributed databases Object detection Data models User preference Recommender systems

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