2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)最新文献_第3页

Accelerating DNN Inference by Edge-Cloud Collaboration 通过边缘云协作加速深度神经网络推理

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) Pub Date : 2021-10-29 DOI: 10.1109/IPCCC51483.2021.9679434

Jianan Chen, Q. Qi, Jingyu Wang, Haifeng Sun, J. Liao

{"title":"Accelerating DNN Inference by Edge-Cloud Collaboration","authors":"Jianan Chen, Q. Qi, Jingyu Wang, Haifeng Sun, J. Liao","doi":"10.1109/IPCCC51483.2021.9679434","DOIUrl":"https://doi.org/10.1109/IPCCC51483.2021.9679434","url":null,"abstract":"Deep neural networks (DNN) have become indispensable tools for intelligent applications today. The demand for deploying DNN on the edge devices increases dramatically. Unfortunately, it is challenging because the DNN inference is computation-intensive, but edge devices are always resource-constraint. Prior solutions attempted to address these challenges with collaboration between cloud and edge devices, but they do not take the inference request rate into account. However, the inference delay will increase dramatically while the request rate becomes higher.In this paper, we propose a scheme to dynamic partition DNN into two or three parts and distribute them at the edge and cloud, achieving the lowest delay with the change of request rate. The scheme selects the optimal partition points of DNN with a layer evaluation model (LEM) and a total delay prediction model (DPM) under different request rates. The experiments of distributed deploying AlexNet, VGG, NiN and ResNet DNN models on image classification dataset ImageNet show that the proposed scheme significantly reduces the total end-to-end latency by fully using both the edge and cloud resources. It reduces the inference delay by 1.3 to 1.6 times and improves the throughput 1.2 to 1.7 times compared to the state of art partition approach.","PeriodicalId":125158,"journal":{"name":"2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129581735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Encrypted All-reduce on Multi-core Clusters 加密的All-reduce在多核集群

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) Pub Date : 2021-10-29 DOI: 10.1109/IPCCC51483.2021.9679399

Mohsen Gavahi, Abu Naser, Cong Wu, M. S. Lahijani, Zhi Wang, Xin Yuan

引用次数: 0

FedVF: Personalized Federated Learning Based on Layer-wise Parameter Updates with Variable Frequency FedVF:基于分层参数变频更新的个性化联邦学习

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) Pub Date : 2021-10-29 DOI: 10.1109/IPCCC51483.2021.9679416

Yuan Mei, Binbin Guo, Danyang Xiao, Weigang Wu

{"title":"FedVF: Personalized Federated Learning Based on Layer-wise Parameter Updates with Variable Frequency","authors":"Yuan Mei, Binbin Guo, Danyang Xiao, Weigang Wu","doi":"10.1109/IPCCC51483.2021.9679416","DOIUrl":"https://doi.org/10.1109/IPCCC51483.2021.9679416","url":null,"abstract":"Federated learning is a new and increasingly popular distributed machine learning paradigm, which can employ multiple clients such as mobile phones to collaboratively train a deep neural network model under the coordination of the central server. The raw data is always kept locally on the clients, and only the model parameters are communicated between clients and the server, so that data privacy can be largely preserved. To cope with the effect of not independent and identically distributed (Non-IID) data, personalized federated learning that can provide personalized and customized models for clients participating in federated training has been proposed and widely studied. In this paper, we propose a personalized federated learning algorithm that can provide a personalized local model for each client while storing the latest global model on the central server through only one federated training. The deep neural network model is divided into two parts of global layers and personalized layers, and the whole training process is partitioned into earlier and later stages. Based on the division of layer-wise parameters, the cumulative learning strategy of updating parameters on different layers with variable frequency is adopted to better personalize local models in the later stage based on the global features learned in the earlier stage. Compared with existing personalized federated learning algorithms, our proposed algorithm can achieve a good balance between the personalized model and the global model in federated learning, and performs better in communication efficiency and model accuracy.","PeriodicalId":125158,"journal":{"name":"2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133569803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Spectral Data Classification By One-Dimensional Convolutional Neural Networks 基于一维卷积神经网络的光谱数据分类

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) Pub Date : 2021-10-29 DOI: 10.1109/IPCCC51483.2021.9679444

Fanguo Zeng, Wen Peng, Gaobi Kang, Zekai Feng, Xuejun Yue

{"title":"Spectral Data Classification By One-Dimensional Convolutional Neural Networks","authors":"Fanguo Zeng, Wen Peng, Gaobi Kang, Zekai Feng, Xuejun Yue","doi":"10.1109/IPCCC51483.2021.9679444","DOIUrl":"https://doi.org/10.1109/IPCCC51483.2021.9679444","url":null,"abstract":"Despite the dominance of Chemometric methods and traditional machine learning algorithms in the field of one-dimensional (1D) spectral data analysis for decades, they still rely on trivial pre-processing steps and hand-crafted feature selections based on domain expertise. Deep two-dimensional convolutional neural networks (2D CNNs) which utilize automatic feature extraction for maximum accuracy have recently achieved overwhelming success in areas such as computer vision and natural language processing. Nevertheless, the 2D CNNs can not be directly applied to the 1D spectral data classification tasks restrained by both the lower dimensionality and scarce samples. Consequently, several dedicated adaptations must be made to ensure the successful implementation of 1D CNN. In this study, a shallow 1D CNN, aiming to attain a compromise between high performance and ease of implementation, was presented. Then, an effective strategy to select architecture hyperparameters and facilitate the fine-tuning process was proposed. Finally, a real case of spectral classification of corn seed viability using visible near-infrared (Vis-NIR) data was carried out, and the 1D CNN achieved 93.3% accuracy, which was superior to the PLS-DA (91.1%) and SVM (86.7%). The results from this study demonstrate that 1D CNN with a highly compact architecture can be devised to effectively classify spectral data in domain-specific tasks.","PeriodicalId":125158,"journal":{"name":"2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122270465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Key-Based Input Transformation Defense Against Adversarial Examples 针对对抗性示例的基于键的输入转换防御

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) Pub Date : 2021-10-29 DOI: 10.1109/IPCCC51483.2021.9679424

Yi Qin, Chuan Yue

引用次数: 2

LAFS: Learning-Based Application-Agnostic Flow Scheduling for Datacenters 基于学习的数据中心应用不可知流调度

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) Pub Date : 2021-10-29 DOI: 10.1109/IPCCC51483.2021.9679437

Feixue Han, Qing Li, Keke Zhu, Jianer Zhou, Yong Jiang, Zhuyun Qi, Fuliang Li

引用次数: 0

SNIS: Storage-Network Iterative Simulation for Disaggregated Storage Systems 离散存储系统的存储-网络迭代仿真

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) Pub Date : 2021-10-29 DOI: 10.1109/IPCCC51483.2021.9679397

Danlin Jia, Tengpeng Li, Xiaoqian Zhang, Li Wang, Mahsa Bayati, Ron Lee, B. Sheng, N. Mi

{"title":"SNIS: Storage-Network Iterative Simulation for Disaggregated Storage Systems","authors":"Danlin Jia, Tengpeng Li, Xiaoqian Zhang, Li Wang, Mahsa Bayati, Ron Lee, B. Sheng, N. Mi","doi":"10.1109/IPCCC51483.2021.9679397","DOIUrl":"https://doi.org/10.1109/IPCCC51483.2021.9679397","url":null,"abstract":"In recent years, designs and optimizations on disaggregated storage systems supported by cutting-edge storage and network techniques emerge dramatically. However, conducting experiments in a disaggregated architecture is often expensive. A comprehensive modeling system of disaggregated storage systems is indispensable for researchers to construct fast and reliable experiments. Modeling and evaluating the performance of disaggregated storage systems is a challenge for the following reasons. First, the performance of a disaggregated storage system depends on network protocols and storage solutions jointly. Second, the available trace datasets for generating the workload may not suffice the need for the simulation, as they are collected without considering the integration of network delay and storage processing time. This work proposes a storage-network iterative simulation (SNIS) for disaggregated storage systems by considering the issues above. Our simulation methodology integrates storage and network simulations to model the end-to-end performance of disaggregated storage systems and conducts multiple rounds of simulations to update arrival times of read/write requests. The evaluation results show that SNIS can converge to a relatively stable state after a certain number of iterations.","PeriodicalId":125158,"journal":{"name":"2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114279085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unravel: An Anomalistic Crowd Investigation Framework Using Social Airborne Sensing 解开:使用社会机载传感的异常人群调查框架

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) Pub Date : 2021-10-29 DOI: 10.1109/IPCCC51483.2021.9679451

Md. Tahmid Rashid, Dong Wang

{"title":"Unravel: An Anomalistic Crowd Investigation Framework Using Social Airborne Sensing","authors":"Md. Tahmid Rashid, Dong Wang","doi":"10.1109/IPCCC51483.2021.9679451","DOIUrl":"https://doi.org/10.1109/IPCCC51483.2021.9679451","url":null,"abstract":"In the domain of smart city applications, anomalistic crowd investigation in public places is a crucial task for the efficient management of unforeseen crowds. Many of the current approaches for crowd monitoring utilize physical sensor-based tracking (e.g., cellular signals, cameras, RFID beacons) to detect abrupt crowds. Unfortunately, these solutions are highly application-specific and are able to only detect the mere presence of crowds without determining their causes that may reveal the actual story behind gatherings that occur suddenly. In this paper, we explore the opportunity to meld two complementary sensing paradigms: social sensing and unmanned aerial vehicle (UAV)-based physical sensing to scrutinize abrupt crowd events occurring in public places. However, two technical challenges exist in developing our solution: i) handling the data sparsity across social sensing platforms and within video footage from UAVs; and ii) the accurate inference of the cause of crowd events from the collected data. To address these challenges, we develop Unravel, an intelligent social airborne sensing (SAS) framework that leverages interdisciplinary techniques from natural language processing (NLP), object recognition, and estimation theory for a comprehensive anomalistic crowd investigation. The evaluation results with a real-world anomalous crowd instance dataset show that the Unravel framework outperforms the state-of-the-art baselines in accurately validating the existence of anomalistic crowd instances in public areas and deriving their causes.","PeriodicalId":125158,"journal":{"name":"2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122721872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Two-layer Federated Learning for Scene Text Detection 场景文本检测的两层联邦学习

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) Pub Date : 2021-10-29 DOI: 10.1109/IPCCC51483.2021.9679372

Haolin Wang, X. Xiao, Yilong Hui, Zhisheng Yin, Nan Cheng

{"title":"Two-layer Federated Learning for Scene Text Detection","authors":"Haolin Wang, X. Xiao, Yilong Hui, Zhisheng Yin, Nan Cheng","doi":"10.1109/IPCCC51483.2021.9679372","DOIUrl":"https://doi.org/10.1109/IPCCC51483.2021.9679372","url":null,"abstract":"Incident scene text detection, as the most crucial step of an incident scene text recognition system, has received increasing research attention. In this paper, a two-layer mobile federated learning model (TMFL) is proposed to protect data privacy and improve training efficiency. Particularly, a fast scene text detector is proposed to detect the multi-directional and multi-scale text by using an asymmetric convolution based feature pyramid network (AC-FPN). Compared with the traditional feature pyramid, asymmetric convolutions can effectively extract rotation-invariant features to improve the model's robustness to directed text. Moreover, in order to achieve a balance between the detection accuracy and efficiency, we modify the lightweight backbone of mobilenetv3, and integrate it with the asymmetric convolution based feature pyramid. In addition, we evaluate the performance of our detector on three benchmark datasets, where the results show that both the accuracy and the speed can be improved. Our detector can achieve an F-measure of 87.8 on the ICDAR2013, 80.5 on the MSRA-TD500 and 84.1 on the ICDAR2015 dataset, running at 32.5 FPS.","PeriodicalId":125158,"journal":{"name":"2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115495942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Q-learning-based Opportunistic Communication for Real-time Mobile Air Quality Monitoring Systems 基于q学习的实时移动空气质量监测系统机会通信

2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) Pub Date : 2021-10-29 DOI: 10.1109/IPCCC51483.2021.9679398

Trung Thanh Nguyen, Truong Thao Nguyen, T. A. Dinh, T. Nguyen, Phi-Le Nguyen

引用次数: 2