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A Bit Level Acceleration of Mixed Precision Neural network

A Bit Level Acceleration of Mixed Precision Neural Network

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International conference on parallel and distributed Systems (ICPADS)

作者： Dehui Qiu Jing Wang Yi Hou Weigong Zhang Lan Gao Information Engineering College Capital Normal University Beijing China School of Information Renmin University of China Beijing China

With the growth of the convolutional neural network (CNN) parameters, the hardware resources become limited when deploying CNN models. Single bit-width quantization may lead to degradation of accuracy, while mixed-precision quantization models maintain higher accuracy. However, current mixed-precision quantization accelerators only consider the algorithm level quantization and fixed-bit-width processing elements (PEs) without fully utilizing the resources. Therefore, we propose a neural network acceleration architecture based on bit-level computational units to improve resource utilization and throughput of mixed-precision quantization accelerators. The 2-bit and 3-bit low-bit computational units are designed to implement the high-bit quantization. We also propose spatio-temporal fusion to satisfy the unique bit widths in each layer with mixed precision quantization. In particular, we use the 2-bit and 3-bit computational units to achieve dynamic layer-level quantization. We also discuss various combinations of different bit widths, which can be dynamically implemented according to the requirements of accuracy, execution time, etc. The proposed acceleration architecture is implemented in Verilog, verified using three network models: ResNet18, VGG7 and LeNet-5, and tested against three accelerators: Eyeriss, Stripes and Bit Fusion. The experimental results show that our accelerators provide more accuracy and the grouping operation reduces the area overhead. It provides a 3.08 to 3.54 times acceleration ratio and 2.4 to 2.57 times reduction in energy consumption on the three networks.

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ProMD: A Proactive Intrusion Response System for Enterprise network with Multi-Domain

ProMD: A Proactive Intrusion Response System for Enterprise ...

引用

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

作者： Siyuan Leng Fenghua Li Lin Zhang Yitong Wen Yunchuan Guo Liang Fang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Enterprise networks are divided into sub-domain structures for resource aggregation and trust dependency. However, the complex domain structure results in delayed and incomplete defenses from Intrusion Response Systems(IRS). This paper proposes a proactive intrusion response system specifically designed for multi-domain networks called ProMD. A Bayesian Risk Graph is designed to analyze and evaluate the risks dynamically. The paper formalizes the selection of countermeasures as a multi-objective optimization problem (MOOP) and proves it is NP-Hard. Therefore a genetic-based heuristic algorithm OPGA is designed to generate optimal response policy sets and an unique policy is selected based on the Entropy Weight method. Experimental results demonstrate that the OPGA outperforms benchmark algorithms.

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ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce NAS Space for Heterogeneous Pipeline of CNNs

ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce ...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Huijuan Duan Danghui Wang Xingting Zhao Kuizhi Mei Jihe Wang School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

Efficiently deploying neural network models on heterogeneous edge systems faces unique challenges. Two major issues are underutilized pipelines caused by hardware heterogeneity and explosive growth of architecture search space. To address these challenges, we propose ExpoNAS, a framework enabling fast deployment-oriented neural architecture search on heterogeneous platforms. ExpoNAS evaluates model partitioning and deployability before search to meet accuracy and latency constraints. However, repeated partitioning leads to exponential growth in architecture search space. We first observe a strong correlation between operational exposure and accuracy improvement, and then we propose an exposure-based reduction technique that excludes the operations of low exposure. By removing these non-critical operations, the exposure-based pruning successfully reduces architecture search time. Our experiments on popular datasets show that the ExpoNAS pipelined design improves execution efficiency by a factor of 2 compared to traditional batch processing. Without compromising accuracy, ExpoNAS is able to reduce the architecture search time by 47.5% for discovering high-accuracy networks, compared to the SOTA NAS strategies.

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MiddleCache: Accelerating TCP based In-memory Key-value Stores using eBPF

MiddleCache: Accelerating TCP based In-memory Key-value Stor...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Yiren Pang Sheng Chen Wenxin Li Hao Liu Yulong Li Xin He Song Zhang Zewei Guan Lide Suo Yuan Liu Tianjin University Tianjin China

In-memory key-value stores are widely used in modern web services to support large-scale user requests by caching popular data. Their performance is critical, and BMC, the state-of-the-art work, builds an in-kernel cache and processes requests before the stack using eBPF to reduce the overhead of the kernel network stack. However, BMC fails to support stateful protocol TCP because pre-stack processing creates TCP state bias between the client and *** is widely used by in-memory key-value stores, is even the only choice for some applications (e.g., Redis), and also suffers from performance issues. In this work, we present MiddleCache, a TCP-enabled in-memory key-value store acceleration design. Our key observation is that the TCP state bias of the client and server can be inferred and eliminated with packet length. The design of MiddleCache has two key parts: (i) A compact TCP state maintenance mechanism that accumulates packet lengths and applies corrections to the packet header, which realize TCP support within the constrains of eBPF. (ii) Lock-free accumulation counters that support high-performance concurrent access by utilizing Receive Side Scaling (RSS). Our experiments show that, compared with Memcached, MiddleCache reduces 56% processing latency on cache hit and achieves a 3.8× throughput improvement on Facebook-like small-size requests workload.

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching

FERI: Feature Enhancement and Relational Interaction for Ima...

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International conference on parallel and distributed Systems (ICPADS)

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Shuo Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method.

关键词： Computer vision Fuses Image matching Semantics Feature extraction Natural language processing

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GLOBAL-LOCALIZED AGENT GRAPH CONVOLUTION FOR MULTI-AGENT REINFORCEMENT LEARNING

GLOBAL-LOCALIZED AGENT GRAPH CONVOLUTION FOR MULTI-AGENT REI...

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IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Liu, Yuntao Dou, Yong Shen, Siqi Qiao, Peng Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781728176055

A lot of efforts have been devoted to solving the problem about complex relationship and localized cooperation among a large number of agents in large-scale multi-agent systems. However, global cooperation among all agents is also important while interactions between agents often happen locally. It is a challenging problem to enable agent to learn global and localized cooperate information simultaneously in multi-agent systems. In this paper, we model the global and localized cooperation among agents by global and localized agent graphs and propose a novel graph convolutional reinforcement learning mechanism based on these two graphs which allows each agent to communicate with neighbors and all agents to cooperate at the high level. Experiments on the largescale multi-agent scenarios in StarCraft II show that our proposed method gets better performance compared with state-of-the-art algorithms and allows agents learning to cooperate efficiently.

关键词： Machine Learning Reinforcement Learning Multi-agent System Graph Convolutional network StarCraft II

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FPGAs-based Edge Computing with distributed Leader Election on Internet of Things

FPGAs-based Edge Computing with Distributed Leader Election ...

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South American Colloquium on Visible Light Communications (SACVLC)

作者： Daniela Vera Karel Toledo Sandy Bolufé Christian Fernández-Campusano Department of Electrical Engineering University of Santiago of Chile (USACH) Santiago Chile

This paper presents a flexible architecture for the Internet of Things (IoT) with edge computing based on Field Programmable Gate Arrays (FPGAs) and distributed leader election (DLE). An enhanced DLE algorithm is introduced which implicitly monitors the communication nodes on the wireless sensor networks (WSNs) and chooses the appropriate leader to improve the reliability of the system. At the edge computing layer, devices rely on hardware description language to leverage the versatility and parallel processing capabilities of FPGAs. We also perform simulations and hardware implementation to evaluate the performance of specific tasks. Numerical results evidence the rise of the leader and how the data from network nodes are utilized to distinguish signals from noise successfully.

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Porting SYCL accelerated neural network frameworks to edge devices 23

Porting SYCL accelerated neural network frameworks to edge d...

引用

International conference on OpenCL (IWOCL)

作者： Gonzalez, Hector Arroyo Angus, Dylan Goli, Mehdi Keir, Paul Riordan, James Georgiev, Svetlozar Univ West Scotland Glasgow Lanark Scotland Codeplay Edinburgh Midlothian Scotland

ISBN: (纸本)9798400707452

Portable hardware acceleration has become increasingly necessary with the rise of the popularity of edge computing. Edge computing, referring to the distributed computing paradigm that encourages data to be processed and stored as close to the source of origination as possible, is needed in areas where bandwidth and latency are restricted and network stability, privacy, or security are unreliable or insecure. Examples of such situations are autonomous mobile robotics, such as autonomous tractors, which often have numerous cameras connected to the host, all needing processing in areas where there can be no reliable connection to a cloud-based platform. Additionally, bridge surveying drones, where mapping and path-planning are needed with low latency, can benefit from a lightweight, compact, low-powered device, especially when there are size and energy consumption requirements. Thus, edge devices, which work as small but compact computers, leverage onboard accelerators to tackle various Robotics, Computer Vision and AI tasks directly on the device without needing an external connection. These accelerators often take the popular form of a GPU like Nvidia's Jetson development kit series, which are driven by the same workflows of Nvidia's AI software and cloud-native frameworks while staying lean, compact and less energy-demanding. However, with the increasing popularity of FPGAs, in the future we could see more edge devices like AMD and Xilinx's KR260 robotics development kit, that operate at low power. Hence, with the surge of the usefulness of edge devices and variety in the brand and type of accelerators, the need for hardware portability in edge devices expands as well. Thus, as we will show in this talk, SYCL as an open-standard, high-level parallel programming model which provides portability not only at the API level but also at the compiler level provides this hardware portability by enabling the same software to be run on both CPU, GPU and FPGA-based edge devic

关键词： SYCL Machine Learning Edge Devices Nvidia Jetson Neural networks ONNX

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GANDPS: Ensuring Both Detection Precision and Stability in GAN-based Anomaly Detection for network Traffic

GANDPS: Ensuring Both Detection Precision and Stability in G...

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

作者： Liuhuan Li Ximin Yang Xiao Zhang Yumeng Yang Xuefeng Chen Wan Tang School of Computer Science South-Central Minzu University Wuhan China Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission China China Telecom Group GuiZhou Co. Ltd Guiyang China

With the ever-growing network traffic and the vast amount of abnormal traffic being created, anomaly detection methods have attracted close attention in the cybersecurity domain. Generative adversarial networks (GANs) have been applied to network traffic anomaly detection, but obtaining high-precision and consistently stable detection results is still challenging for current methods. In the paper, we propose an improved GAN-based anomaly detection method called GANDPS for network traffic. The improved GAN introduces an encoder based on the original GAN, and a specialized layer is integrated into the encoder to process abnormal data. This improvement enables the encoder to effectively capture the features of normal data while distinguishing the features of abnormal data. In addition, an analogous reconstruction training strategy is employed between the generator and the encoder to balance the adversarial training between the generator and the discriminator, thereby improving the detection precision and stability of the GAN-based model. This comprehensive enhancement not only effectively elevates the detection performance of our method, but also ensures stable detection results. Experimental results on public datasets demonstrate that the proposed GANDPS outperforms multiple GAN-based methods in terms of detection precision, recall, and F1-score, and the detection results are stabilized within acceptable ranges.

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Key technology research of distribution automation system based on cloud architecture 9

Key technology research of distribution automation system ba...

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9th China International conference on Electricity Distribution, CICED 2021

作者： Su, Biaolong Wu, Huichao Zhang, Yiran Zhou, Yanghao NARI Group Corporation State Grid Electric Power Research Institute China

ISBN: (纸本)9781728192291

China's economic development has higher requirements for the power supply capability and reliability of distribution network. At present, the management scope of distribution automation system cannot cover the whole distribution network and many regional electric companies cannot manage the operation state of distribution network through the existing distribution automation system, which restricts the development of distribution network construction. It is necessary to develop a new system construction scheme to cope with the different data capacity and function requirements of distribution networks in different cities. This paper proposes a technical scheme of main station of distribution automation system based on cloud architecture in aspects of system architecture, elastic data processing, safe access of distribution terminals and optimal operation control. With the distributed collaboration between core cloud and regional cloud as well as data fragmentation parallel processing technology, the problem of massive access and application of distribution network will be solved. © 2021 IEEE.

关键词： Cloud computing architecture

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