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PC-BNA: Parallel Convolution Binary Neural Network Accelerator

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Haihui Liu Yuang Zhang Xiangwei Zheng Mengxin Yu Baoxu An Xiaojie Liu Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University Jinan China

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. Therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. The input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. The experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. The image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. The results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications.

关键词： Performance evaluation Convolution Neural networks Digital signal processors Speech recognition Logic gates Mobile handsets

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DistStream: An Order-Aware distributed Framework for Online-Offline Stream Clustering Algorithms 40

DistStream: An Order-Aware Distributed Framework for Online-...

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40th ieee international conference on distributed computing systems (ICDCS)

作者： Xu, Lijie Ye, Xingtong Kang, Kai Guo, Tian Dou, Wensheng Wang, Wei Wei, Jun Chinese Acad Sci Inst Software State Key Lab Comp Sci Guangzhou Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Technol Nanjing Peoples R China Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9781728170022

Stream clustering is an important data mining technique to capture the evolving patterns in real-time data streams. Today's data streams, e.g., IoT events and Web clicks, are usually high-speed and contain dynamically-changing patterns. Existing stream clustering algorithms usually follow an online-offline paradigm with a one-record-at-a-time update model, which was designed for running in a single machine. These stream clustering algorithms, with this sequential update model, cannot be efficiently parallelized and fail to deliver the required high throughput for stream clustering. In this paper, we present DistStream, a distributed framework that can effectively scale out online-offline stream clustering algorithms. To parallelize these algorithms for high throughput, we develop a mini-batch update model with efficient parallelization approaches. To maintain high clustering quality, DistStream's mini-batch update model preserves the update order in all the computation steps during parallel execution, which can reflect the recent changes for dynamically-changing streaming data. We implement DistStream atop Spark Streaming, as well as four representative stream clustering algorithms based on DistStream. Our evaluation on three real-world datasets shows that DistStream-based stream clustering algorithms can achieve sublinear throughput gain and comparable (99%) clustering quality with their single-machine counterparts.

关键词： Stream clustering data stream scalability

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distributed Threat Intelligence at the Edge Devices: A Large Language Model-Driven Approach

Distributed Threat Intelligence at the Edge Devices: A Large...

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ieee Annual international Computer Software and Applications conference (COMPSAC)

作者： Syed Mhamudul Hasan Alaa M. Alotaibi Sajedul Talukder Abdur R. Shahid School of Computing Southern Illinois University Carbondale IL USA Secure and Trustworthy Intelligent Systems (SHIELD) Lab Center for Research and Education in AI and Cybersecurity (CARE-AI-C)

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

With the proliferation of edge devices, there is a significant increase in attack surface on these devices. The decen-tralized deployment of threat intelligence on edge devices, coupled with adaptive machine learning techniques such as the in-context learning feature of Large Language Models (LLMs), represents a promising paradigm for enhancing cybersecurity on resource-constrained edge devices. This approach involves the deployment of lightweight machine learning models directly onto edge devices to analyze local data streams, such as network traffic and system logs, in real-time. Additionally, distributing computational tasks to an edge server reduces latency and improves responsiveness while also enhancing privacy by processing sensitive data locally. LLM servers can enable these edge servers to autonomously adapt to evolving threats and attack patterns, continuously updating their models to improve detection accuracy and reduce false positives. Furthermore, collaborative learning mechanisms facilitate peer-to-peer secure and trustworthy knowledge sharing among edge devices, enhancing the collective intelligence of the network and enabling dynamic threat mitigation measures such as device quarantine in response to detected anomalies. The scalability and flexibility of this approach make it well-suited for diverse and evolving network environments, as edge devices only send suspicious information such as network traffic and system log changes, offering a resilient and efficient solution to combat emerging cyber threats at the network edge. Thus, our proposed framework can improve edge computing security by providing better security in cyber threat detection and mitigation by isolating the edge devices from the network.

关键词： Adaptation models Image edge detection Computational modeling Prevention and mitigation Telecommunication traffic Threat assessment Software

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A Serverless computing Fabric for Edge & Cloud

A Serverless Computing Fabric for Edge & Cloud

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ieee international conference on Cognitive Machine Intelligence (CogMI)

作者： Stefan Nastic Philipp Raith Alireza Furutanpey Thomas Pusztai Schahram Dustdar Distributed Systems Group TU Wien Vienna Austria

Serverless computing has been establishing itself as a compelling paradigm for the development and of modern cloud-native applications. Serverless represents the next step in the evolution of cloud programming models, services and platforms, which is especially appealing due to its low management overhead, easy deployment, scale-to-zero and the promise of optimized costs. Recently, due to the advantages it offers, the serverless paradigm has been growing beyond traditional clouds, making its way to the Edge. The natural evolutionary step for serverless computing is to unify the Edge and the Cloud into what we refer to as Edge-Cloud Continuum. In this paper, we outline our vision of the Serverless computing Fabric (SCF) for the Edge-Cloud continuum. We introduce the reference architecture for the SCF and show how it unlocks the full potential of the Edge-Cloud continuum. We also discuss main opportunities and challenges, which need to be overcome in order to achieve the vision of the Serverless computing Fabric. Finally, we introduce key design principles together with core enabling runtime mechanisms, which are intended to serve as a research road map towards the Serverless computing Fabric for Edge-Cloud continuum.

关键词： Runtime Costs Roads Serverless computing Computer architecture Programming Reliability engineering

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Energy-Efficient Resource Allocation for Relay-Assisted Mobile Edge computing systems

Energy-Efficient Resource Allocation for Relay-Assisted Mobi...

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Satellite computing (Satellite), ieee international conference on

作者： Jialun Shi Shuang Chen Han Chen Fengdi Wang Meihui Hua Gaofeng Nie State Grid Fujian Information Telecommunication Company Beiing Smartchip Microelectronics Technology Company Limited State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommun. Beijing China

To meet the requirements of computation intensive and latency sensitive applications, mobile edge computing (MEC) is utilized to improve quality of service, which allows user equipments (UEs) to offload urgent tasks to adjacent edge base stations (BSs) for efficient data processing. Considering the deterioration of wireless channels caused by user mobility and obstacles, this paper investigates a relay-assisted MEC system, where a relay is deployed between UE and BS with better channel condition. UE has three modes for data computing, i.e., local computing, relay-assisted offloading, and direct transmission offloading. With the aim of energy consumption minimization, an iterative and low-complexity algorithm is proposed by jointly optimizing task partition, local CPU frequency, bandwidth allocation, and power control at both UE and relay, and the closed-form optimal solutions are derived. Numerical results validate that the designed algorithm outperforms static and fractional power control schemes, and the relay-assisted MEC system can significantly decrease energy consumption in comparison with that without the assistance of relay.

关键词： Wireless communication Energy consumption Wireless sensor networks Satellites Multi-access edge computing Power control Partitioning algorithms

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Sound-LDR sensors Empowered Secure True Random Number Generator for PoS Consensus in Blockchain

Sound-LDR Sensors Empowered Secure True Random Number Genera...

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ieee international conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Haozhe Chai Jun Wu Qianqian Pan Xinzheng Feng Graduate School of Information Production and Systems Waseda University Fukuoka Japan School of Engineering The University of Tokyo Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350389524

ISBN: (纸本)9798350389531

In a decentralized environment of blockchain, people usually select a random node to perform bookkeeping with the Proof of Stake (PoS) consensus mechanism. To randomly select miners and validators and ensure fair distribution of rewards, the algorithm must incorporate a fair, unbiased random number source. Therefore, in many PoS consensus mechanisms, random numbers are a critical technology. PoS consensus faces the following problems in random number generation: Firstly, in the PoS system, if the number and order of the selected verification nodes are predicted by the attacker, the system will be vulnerable to attacks. Second, if the random number generation algorithm has loopholes or is cracked by an attacker, the attacker may use these loopholes to attack the network, resulting in the insecurity of the PoS system. To address the challenges above, this paper proposes a low-cost, light-weighted, true random number generator designed by a sensors that detects non-deterministic signals. The random numbers generated through this method passed the NIST-STS randomness test. The true random number generator is applied to the commit-reveal service in PoS consensus to randomly elect block producer. Using the random number generated can also enhance IoT security because Photoresistor sensors have applications in IoT systems for smart building. The random number generated by the IoT device can be used as the basis for randomness proof on the blockchain. And the cloud can also use the randomness for identity verification, secure event tracing, data integrity and tamper resistance.

关键词： Resistance Smart buildings Prediction algorithms sensor systems and applications Generators Consensus protocol Internet of Things

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingyu Liu Yijie Wang Xiaohui Zhou Yongjun Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors.

关键词： Learning systems Time-frequency analysis Fast Fourier transforms Time series analysis Signal processing Feature extraction Entropy Iterative methods Speech processing Anomaly detection

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Performance Prediction of Sparse Matrix Multiplication on a distributed BigData Processing Environment 1

Performance Prediction of Sparse Matrix Multiplication on a ...

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1st ieee international conference on Autonomic computing and Self-Organizing systems (ACSOS)

作者： Park, Jueon Lee, Kyungyong Kookmin Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9781728184142

Sparse matrix multiplication (SPMM) is widely used for various machine learning algorithms. With advancements in big-data processing, the importance of distributed SPMM processing becomes important for handling large-scale datasets. We conducted thorough experiments using various distributed SPMM implementations and discovered considerable performance variations for distinct datasets and scenarios. To provide an optimal SPMM execution environment, we propose features that represent SPMM task characteristics. Using these features, we propose building a tree-based non-linear gradient boosting (GB) regressor model that presents superb prediction accuracy across diverse distributed SPMM implementations and datasets.

关键词： Learning algorithms

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Electronic System Design of a Formula Student Electric Car

Electronic System Design of a Formula Student Electric Car

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2020 ieee international conference on distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020

作者： Sivakumar, Anirudh Mohanty, Pratik Manipal Institute of Technology Manipal Academy of Higher Education Mechatronics Engineering Department ManipalKarnataka576104 India Manipal Institute of Technology Manipal Academy of Higher Education Electronics and Communications Engineering Department ManipalKarnataka576104 India

ISBN: (纸本)9781728198859

This paper addresses the design of the Data Acquisition System, Battery Management System and the different safety circuits present in a Formula Student Electric Vehicle. The car was designed around the rules and regulations of the Formula Bharat 2019 Competition while keeping efficiency and cost in mind. The data acquisition system was designed to utilize the sensor network in the car, in real-time, based on a master-slave topology. A robust battery management system that monitors current, voltage, temperature and SoC of the battery pack and maintains a safe functionality was required and three safety circuit systems for fault analysis and alarming in case of any electrical malfunctions. These systems are brought together with the help of the wiring harness to keep it as compact and light-weight as possible. © 2020 ieee.

关键词： sensor networks

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Local-Adaptive Transformer for Multivariate Time Series Anomaly Detection and Diagnosis

Local-Adaptive Transformer for Multivariate Time Series Anom...

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ieee international conference on systems, Man and Cybernetics

作者： Xiaohui Zhou Yijie Wang Hongzuo Xu Mingyu Liu Ruyi Zhang National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

Time series data are pervasive in varied real-world applications, and accurately identifying anomalies in time series is of great importance. Many current methods are insufficient to model long-term dependence, whereas some anomalies can be only identified through long temporal contextual information. This may finally lead to disastrous outcomes due to false negatives of these anomalies. Prior arts employ Transformers (i.e., a neural network architecture that has powerful capability in modeling long-term dependence and global association) to alleviate this problem; however, Transformers are insensitive in sensing local context, which may neglect subtle anomalies. Therefore, in this paper, we propose a local-adaptive Transformer based on cross-correlation for time series anomaly detection, which unifies both global and local information to capture comprehensive time series patterns. Specifically, we devise a cross-correlation mechanism by employing causal convolution to adaptively capture local pattern variation, offering diverse local information into the long-term temporal learning process. Furthermore, a novel optimization objective is utilized to jointly optimize reconstruction of the entire time series and matrix derived from cross-correlation mechanism, which prevents the cross-correlation from becoming trivial in the training phase. The generated cross-correlation matrix reveals underlying interactions between dimensions of multivariate time series, which provides valuable insights into anomaly diagnosis. Extensive experiments on six real-world datasets demonstrate that our model outperforms state-of-the-art competing methods and achieves 6.8%-27.5% $F_{1}$ score improvement. Our method also has good anomaly interpretability and is effective for anomaly diagnosis.

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