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ieee conference on Energy Internet and Energy System Integration (EI2)

作者： Zhaoxia Li Xuejiao Gong Ruijin Zhu Dechang Yang College of Electrical Eengineering Tibet Agricultural and Animal Husbandry University Linzhi China

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

Distributed generator (DG) has been widely integrated into distribution networks (DN) recently, which refers to a solution to the energy crisis and environmental pollution. However, the output of DG has a strong uncertainty, which may lead to the reverse power flow and voltage violation. With the development of power electronic technology, the integration of fully-controlled devices have been brought opportunity for the improvement of the renewable energy consumption ability. In order to quantitative evaluate the renewable energy consumption ability of DN, a unified framework of quantifying renewable energy consumption ability is proposed in this paper. Firstly, the quantification method for renewable energy consumption ability is proposed, including objective function and operational constraints. The improvement of renewable energy consumption ability based on soft open point (SOP) is further discussed and the operation status of SOP are also considered in the framework. Finally, the modified ieee 33 node system is used to verify the effectiveness of the proposed method. Results show that with the integration of SOP, the renewable energy consumption ability is effectively increased.

关键词： Renewable energy sources Uncertainty Distribution networks System integration Linear programming Power electronics Load flow

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DeepBurning-GL: an Automated Framework for Generating Graph Neural network Accelerators

DeepBurning-GL: an Automated Framework for Generating Graph ...

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ieee International conference on Computer-Aided Design

作者： Shengwen Liang Cheng Liu Ying Wang Huawei Li Xiaowei Li University of Chinese Academy of Sciences State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

ISBN: (数字)9781665423243

Building FPGA-based graph learning accelerators is very time- consuming due to the low-level RTL programming and the complicated design flow of FPGA development. It also requires the architecture and hardware expertise from the Graph Neural network (GNN) application developers to tailor efficient accelerator designs on FPGAs. This work proposes an automation framework, DeepBurning-GL, which is compatible with state-of-the-art graph learning frameworks such as Deep Graph Library so that the developers can easily generate application-specific GNN accelerators from the software-described models. First, DeepBurning-GL employs a GNN performance analyzer to locate the performance bottleneck of specific GNN applications and decide the major design architectures and parameters that meet the user-specified constraints. Second, DeepBurning-GL provides a series of pre-built design templates such as computing templates and memory templates, which can be parameterized and fused to generate the final accelerator design. It also includes an optimizer that conducts automatic optimization by adjusting the accelerator architectural parameters. In evaluation, we use DeepBurning-GL to generate customized accelerators on three different FPGA platforms for various GNN models and workloads. The experimental results show that the generated accelerators achieve 179.4X and 40.1X energy-efficiency boost over the CPU and GPU solutions on average and deliver a 6.28X speedup and 6.73X energy-efficiency improvement on average compared to the latest GNN accelerator HyGCN on Alveo U50.

关键词： Field programmable gate arrays Feature extraction Hardware Aggregates Neural networks Computational modeling Automation

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λ-NIC: Interactive Serverless Compute on Programmable SmartNICs

λ-NIC: Interactive Serverless Compute on Programmable Smart...

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International conference on Distributed Computing Systems

作者： Sean Choi Muhammad Shahbaz Balaji Prabhakar Mendel Rosenblum Stanford University

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

There is a growing interest in serverless compute, a cloud computing model that automates infrastructure resource- allocation and management while billing customers only for the resources they use. Workloads like stream processing benefit from high elasticity and fine-grain pricing of these serverless frameworks. However, so far, limited concurrency and high latency of server CPUs prohibit many interactive workloads (e.g., web servers and database clients) from taking advantage of serverless compute to achieve high *** this paper, we argue that server CPUs are ill-suited to run serverless workloads (i.e., lambdas) and present λ-NIC, an open- source framework, that runs interactive workloads directly on a SmartNIC; more specifically an ASIC-based NIC that consists of a dense grid of network Processing Unit (NPU) cores. λ- NIC leverages SmartNIC's proximity to the network and a vast array of NPU cores to simultaneously run thousands of lambdas on a single NIC with strict tail-latency guarantees. To ease the development and deployment of lambdas, λ-NIC exposes an event-based programming abstraction, Match+Lambda, and a machine model that allows developers to compose and execute lambdas on SmartNICs easily. Our evaluation shows that λ- NIC achieves up to 880x and 736x improvements in workloads' response latency and throughput, respectively, while significantly reducing host CPU and memory usage.

关键词： Reduced instruction set computing Computational modeling Pricing programming Throughput Web servers Resource management

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Hybrid Learning with Teacher-student Knowledge Distillation for Recommenders

Hybrid Learning with Teacher-student Knowledge Distillation ...

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ieee International conference on Data Mining Workshops (ICDM Workshops)

作者： Hangbin Zhang Raymond K. Wong Victor W. Chu University of New South Wales Sydney Australia Nanyang Technological University Singapore

ISBN: (数字)9781728190129

ISBN: (纸本)9781728190136

Latent variable models have been widely adopted by recommender systems due to the advancements of their learning scalability and performance. Recent research has focused on hybrid models. However, due to the sparsity of user and/or item data, most of these proposals have convoluted model architectures and objective functions. In particular, the latter are mostly tailored for sparse data from either user or item spaces. Although it is possible to derive an analogous model for both spaces, this makes a system overly complicated. To address this problem, we propose a deep learning based latent model called Distilled Hybrid network (DHN) with a teacher-student learning architecture. Unlike other related work that tried to better incorporate content components to improve accuracy, we instead focus on model learning optimization. To the best of our knowledge, we are the first to employ teacher-student learning architecture for recommender systems. Experiment results show that our proposed model notably outperforms state-of-the-art approaches. We also show that our proposed architecture can be applied to existing recommender models to improve their accuracies.

关键词： Training Knowledge engineering Scalability Linear programming Proposals Recommender systems Optimization

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Cache Optimization Method to Reduce network Traffic in Communication systems 9

Cache Optimization Method to Reduce Network Traffic in Commu...

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9th International conference on Parallel architectures, Algorithms and programming (PAAP)

作者： Chen, Ruey-Shun Chen, Yeh-Cheng Hu, Yu-Xi Horng, Shi-Jinn Fan, Xiaopeng Wang, T-C Xiong, Naixue Dongguan Polytech Dept Comp Engn Dongguan Peoples R China Univ Calif Davis Dept Comp Engn Davis CA 95616 USA Northwestern Polytech Univ Sch Software & Microelect Xian Shaanxi Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Natl Chiao Tung Univ Inst Informat Management Hsinchu Taiwan Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9781538694039

using the cloud technology with Smart Protect network (SPN) to do file virus scanning. The overwhelming increase of population will lead to an increase network traffic usage. In most products, local cache mechanism is implemented for the purpose of reducing the network traffics. However, the database designed to store cache is limited, lot of times, cache will get purged when there are lots of queries sent and replaced by other newest queries. It is not an efficient by using traditional methodology to build or forming cache. In order to reduce the network traffic usage, the paper propose a solution, which utilize data mining technique with clustering concept, by gathering the current feedback data we have from our SPN, we are able to form these data in groups with similarity, and by deploying these data to client side, to achieve the reduction of traffic usage. In prototyping, this design for military communications can really reduce network traffic more than 20%. And the speed of file scanning time can faster more than 12%.

关键词： Military Communications Data Mining Method Cache Optimization network Traffic

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Joint Antenna Array Mode Selection and User Assignment for Full-Duplex MU-MISO Systems

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ieee TRANSACTIONS ON WIRELESS COMMUNICATIONS 2019年第6期18卷 2946-2963页

作者： Nguyen, Hieu V. Van-Dinh Nguyen Dobre, Octavia A. Wu, Yongpeng Shin, Oh-Soon Soongsil Univ Sch Elect Engn Seoul 06978 South Korea Soongsil Univ Dept ICMC Convergence Technol Seoul 06978 South Korea Mem Univ Fac Engn & Appl Sci St John NF A1X3C5 Canada Shanghai Jiao Tong Univ Dept Elect Engn Minhang 200240 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710126 Shaanxi Peoples R China

This paper considers a full-duplex (FD) multiuser multiple-input single-output system where a base station simultaneously serves both uplink (UL) and downlink (DL) users on the same time-frequency resource. The crucial barriers in implementing FD systems reside in the residual self-interference and co-channel interference. To accelerate the use of FD radio in future wireless networks, we aim at managing the network interference more effectively by jointly designing the selection of half-array antenna modes (in the transmit or receive mode) at the base station with time phases and user assignments. The first problem of interest is to maximize the overall sum rate subject to quality-of-service requirements, which is formulated as a highly non-concave utility function followed by non-convex constraints. To address the design problem, we propose an iterative low-complexity algorithm by developing new inner approximations, and its convergence to a stationary point is guaranteed. To provide more insights into the solution of the proposed design, a general max-min rate optimization is further considered to maximize the minimum per-user rate while satisfying a given ratio between UL and DL rates. Furthermore, a robust algorithm is devised to verify that the proposed scheme works well under channel uncertainty. The simulation results demonstrate that the proposed algorithms exhibit fast convergence and substantially outperform existing schemes.

关键词： Full-duplex radios multiuser transmission non-convex programming robust design self-interference spectral efficiency transmit beamforming user assignment

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Distributed representation learning of knowledge graph with diverse information 9

Distributed representation learning of knowledge graph with ...

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9th International conference on Parallel architectures, Algorithms and programming (PAAP)

作者： Guo, Wenzhong Dai, Yuanfei Chen, Yiyan Chen, Xing Xiong, Neal N. Fuzhou Univ Coll Math & Comp Sci Fuzhou Fujian Peoples R China Fuzhou Univ Key Lab Network Comp & Intelligent Informat Proc Fuzhou Fujian Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK USA

ISBN: (纸本)9781538694039

Knowledge graph is a type of network structure in which nodes represent entities and edges indicate relations. However, as the network size explosively increases, the issues of data sparsity and computation inefficiency on large-scale knowledge graph become more difficult to manipulate and manage. Knowledge graph embedding, which is a representation technique of embedding entities and relations in the knowledge graph into continuous, dense, and low-dimensional semantics vector spaces to tackle these challenges and endow the model with the abilities of knowledge fusion and inference, has recently attracted much attention. In this paper, we firstly introduce the overall framework and specific idea of embedding models. We then introduce two applications that apply KG embedding, compare the performance of the methods in these applications. Finally, we summarize several challenges to overcome, and provide some prospective future research directions such as deep learning network for approaches and applications.

关键词： Knowledge graph embedding Knowledge representation Deep learning Statistical relational learning

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An Architecture-Agnostic Integer Linear programming Approach to CGRA Mapping 18

An Architecture-Agnostic Integer Linear Programming Approach...

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55th ACM/ESDA/ieee Design Automation conference (DAC)

作者： Chin, S. Alexander Anderson, Jason H. Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781450357005

Coarse-grained reconfigurable architectures (CGRAs) have gained traction as a potential solution to implement accelerators for compute-intensive kernels, particularly in domains requiring hardware programmability. Architecture and CAD for CGRAs are tightly intertwined, with many prior works having combined architectures and tools. In this work, we present an architecture-agnostic integer linear programming (ILP) approach for CGRA mapping, integrated within an open-source CGRA architecture evaluation framework. The mapper accepts an application and an architecture description as input and can generate an optimal mapping, if indeed mapping is feasible. An experimental study demonstrates its effectiveness over a range of CGRA architectures.

关键词： Routing Computer architecture Registers Context modeling Multiplexing Radio frequency Computational modeling

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Crystal-Free architectures for Smart Dust and the Industrial IoT

Crystal-Free Architectures for Smart Dust and the Industrial...

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International conference on Internet of Things: Systems, Management and Security

作者： Thomas Watteyne INRIA France

ISBN: (数字)9780738124605

ISBN: (纸本)9781665419260

A crystal-free architecture of a low-power wireless node allows for a small and cheap design. The Single-Chip micro Mote (SCuM) is the first crystal-free mote-on-chip to be compatible with standards such as Bluetooth Low Energy and ieee802.15.4, and develops as a collaboration between Kris Pister’s team at UC Berkeley and Thomas Watteyne’s team at Inria. This keynote will go through our 2019–2020 research journey around the SCuM chip. We start by detailing why a “crystal-free” architecture is such as radical move away from traditional low-power wireless radio design. We discuss the challenges of calibration and compensation in these designs, and show how we get a SCuM chip to communicate with off-the-shelf motes and participate in a standards-based IoT network. We then present an unexpected use case that crystal-free motes enable: tracking Asian hornets as they fly from a beehive back to their nest. We illustrate this presentation with numerous videos and hands-on experience. We finish this talk by discussing why crystal-free micro-robots are the next frontier of IoT research, addressing all open IoT networking research questions at once, which a tremendous potential for pioneering applications such as micro-wearables and swarm robotics. This keynote is tailored to the advanced researcher or engineer in embedded system, IoT and low-power wireless. It draws on recent activities within Inria’s EVA team (***/eva/), UC Berkeley’s Pister lab (***) and their collaborative project around crystal-free architectures (***).

关键词： Wireless communication Telecommunications Standards Communication system security Collaboration Wireless mesh networks Videos

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On the Detection of Community Smells Using Genetic programming-based Ensemble Classifier Chain

On the Detection of Community Smells Using Genetic Programmi...

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International conference on Global Software Engineering (ICGSE)

作者： Nuri Almarimi Ali Ouni Moataz Chouchen Islem Saidani Mohamed Wiem Mkaouer ETS Montreal University of Quebec Montreal QC Canada Rochester Institute of Technology Rochester NY USA

Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as sub-optimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an auto-mated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterize the existence of community smell in-stances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89% achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterize community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per *** CONCEPTS• Software and its engineering → Software organization and properties.

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