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Automatic Implementation of Large-Scale CNNs on FPGA Cluster Based on HLS4ML

Automatic Implementation of Large-Scale CNNs on FPGA Cluster...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Yihang Lu Xingyun Qi Yuan Li Mingche Lai Yankang Zhao Zhenqi Li Hanyuan Li Qiang Wang School of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Convolutional Neural Networks (CNNs) have demonstrated remarkable performance across various computer vision tasks. Due to the computational and data-intensive nature of CNNs, Field-Programmable Gate Arrays (FPGAs) are exceptionally well-suited for accelerating the CNN computation process. However, large-scale CNNs such as ResNet-84 contain an enormous number of parameters that exceed the capacity of a single FPGA, rendering the deployment on a single device impractical. In this paper, we develop an automated end-to-end design flow for mapping large-scale CNNs across multiple FPGAs, on the basis of the HLS4ML dataflow architecture. We propose a graph optimization method to streamline the CNN structure and reduce resource consumption. We also summarize a resource allocation algorithm that automatically determines the specific hardware resources necessary for each CNN layer. Furthermore, we introduce a partitioning methodology capable of effectively segmenting CNNs into multiple FPGAs, and providing each subgraph with specific interfaces to support communication between different FPGAs. To validate the methodology, we construct a multi-FPGA platform interconnected via LVDS. We select two typical networks, ResNet-8 and ResNet-84, as the benchmarks for evaluation. The experimental results demonstrate that our approach significantly outperforms existing solutions. It attains an 18.6-fold increase in speed over Vitis AI, a 2.2-fold improvement over FINN, and a 3.4-fold enhancement over the original single-FPGA HLS4ML implementation for ResNet-8. For ResNet-84, our method achieves a remarkable 33.6-fold speedup over Vitis AI. Additionally, when compared to other non-automated multi-FPGA solutions, our methodology still exhibits significant performance improvements.

关键词： Optimization methods Computer architecture Benchmark testing Rendering (computer graphics) Hardware partitioning algorithms Convolutional neural networks Resource management Artificial intelligence Field programmable gate arrays

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Distributed Economic Dispatch for Multi-area Power Systems Based on Multi-objective Differential Evolution Algorithm

Distributed Economic Dispatch for Multi-area Power Systems B...

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Data Driven Control and Learning Systems (DDCLS)

作者： Xuyang Ren Xuhui Bu Yanling Yin Xuan Li Yan Zhang School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo P. R. China School of Business Administration Henan Polytechnic University Jiaozuo P. R. China College of Business Zhengzhou Technology and Business University Zhengzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Economic dispatch of a multi-area interconnected power system with wind, solar, and energy storage units is a typical non-convex and nonlinear optimization problem, which is difficult to be solved by the existing mathematical programming methods. In this paper, a distributed multi-objective differential evolution algorithm is proposed. Firstly, to improve the global search ability of the multi-objective differential evolutionary algorithm, the population is partitioned using a competitive mechanism. A multi-strategy scheme is used according to the partition to enhance the diversity of the algorithm. Secondly, the IEEE 39-bus system is decomposed into several sub-areas using the bus tearing method and the interval information is transmitted through the boundary tie-lines. Then a distributed method is proposed to be applied to the above algorithm, where each sub-algorithm solves the sub-problem associated with each area. Finally, the IEEE 39-bus system is divided into two-area and three-area interconnected systems, and the performance of the algorithm is verified on the modified IEEE 39-bus system. The experimental results verify the effectiveness of the improved multi-objective differential evolution optimization algorithm and the feasibility of the distributed approach.

关键词： Economics Learning systems Wind energy Sociology Interconnected systems partitioning algorithms Power systems

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Research and Application of Deep Learning Algorithm Based on Multi-Source Data Fusion of Power Grid And Communication Network

Research and Application of Deep Learning Algorithm Based on...

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Advancement in Technology (ICONAT), International Conference for

作者： Li Li Wei Zhou Liang Ma Yingjie Jiang Chao Sun Pu Zhang State Grid Shandong Electric Power Company Information Telecommunications Company Jinan China State Grid Shandong Electric Power Company China

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Multi-source data fusion plays a very important role in improving the intelligent level of power grid operation, but there is a problem that multi-source data fusion is not ideal. The previous operation mode could not solve the problem of data fusion in the intelligent operation of the power grid, and the processing was inaccurate. Therefore, this paper proposes a deep learning algorithm for multi-source data fusion analysis. Firstly, the signal processing and estimation theory are used to integrate the power grid communication data, and the indicators are carried out according to the requirements of multi-source data fusion partitioning to reduce interference factors in multi-source data fusion. Then, signal processing and estimation theory intelligently process power grid communication to form an intelligent processing scheme and fuse multi-source data The results were comprehensively analyzed. MATLAB simulation shows that under the condition of certain processing standards, deep learning algorithms intelligently process power grid communication accuracy and multi-source data fusion time are better than previous operating modes.

关键词： Deep learning Technological innovation Accuracy Data integration Signal processing algorithms Estimation theory Signal processing Power grids partitioning algorithms Communication networks

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Deep Ad-hoc Sub-Team Partition Learning for Multi-Agent Air Combat Cooperation

Deep Ad-hoc Sub-Team Partition Learning for Multi-Agent Air ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Songyuan Fan Haiyin Piao Yi Hu Feng Jiang Roushu Yang Department of Computing Harbin Institute of Technology Harbin China School of Electronics and Information Northwestern Polytechnical University Xian China SAIL Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In the future, unmanned autonomous air combat will encounter large-scale confrontation scenarios, where agents must consider complex time-varying relationships among aircraft when making decisions. Previous works have already introduced Multi-Agent Reinforcement Learning (MARL) into air combat and succeeded in surpassing the human expert level. However, they mainly focus on small-scale air combat with low relationship complexity, e.g., 1-vs-1 or 2-vs-2. As more agents join the confrontation, existing algorithms tend to suffer significant performance degradation due to the increase in problem dimensions. In view of this, this paper proposes Deep Ad-hoc Sub-Team Partition Learning(DASPL) to address large-scale air combat problems. DASPL models multi-agent air combat as a graph to handle the complex relations and introduces an automatic partitioning mechanism to generate dynamic sub-teams, which converts the existing large-scale multi-agent air combat cooperation problem into multiple small-scale equivalence problems. Additionally, DASPL incorporates an efficient message passing method among the participating sub-teams.

关键词： Degradation Message passing Atmospheric modeling Deep reinforcement learning partitioning algorithms Complexity theory Aircraft Intelligent robots

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Brain Tumor Segmentation using Spatial Fuzzy C-means(SFCM) and CNN Fusion

Brain Tumor Segmentation using Spatial Fuzzy C-means(SFCM) a...

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Computational Intelligence for Green and Sustainable Technologies (ICCIGST), International Conference on

作者： Sailaja Narava Veera Reddy Aduru Atluri Pujitha T Rakesh Gemini Bapatla Sumanth Electrical and Electronics Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

In today’s medical field, prompt and precise detection of brain tumors is essential for optimal treatment planning and enhanced patient prognosis. One of the most common methods for diagnosing brain tumors is through SFCM (Spatial Fuzzy C Means) and CNN. SFCM is a clustering algorithm that utilizes fuzzy set theory to partition images into different regions based on similarity characteristics. This algorithm has been frequently utilized in diverse implementations, such as image processing and pattern recognition. Conversely, CNN is an advanced deep-learning technique that has demonstrated impressive efficacy in peculiar computer vision tasks, such as image segmentation. Although both SFCM and CNN have been used individually for brain tumor segmentation, there is a need for an integrated approach that combines the strengths of both methods to enhance segmentation accuracy. To achieve more precise and efficient brain tumor detection, the combination of SFCM and CNN holds great potential. By integrating the feature extraction capabilities of CNN with the segmentation abilities of SFCM, medical imaging can be transformed. This integration eliminates the need for hand-crafted features and allows for processing images of arbitrary sizes, thus enhancing the accuracy and adaptability of the segmentationprocess.

关键词： Image segmentation Accuracy Clustering algorithms Feature extraction partitioning algorithms Planning Pattern recognition Convolutional neural networks Prognostics and health management Medical diagnostic imaging

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Bidirectional Choice for Many-to-many Online Task Assignment in Mobile Crowdsourcing

Bidirectional Choice for Many-to-many Online Task Assignment...

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IEEE International Conference on Web Services (ICWS)

作者： Xinhao Li Yingjie Wang Feng Jiang Yang Gao Chunxiao Mu Zhipeng Cai Yingxin Li Shilong Jin School of Computer and Control Engineering Yantai University Yantai China Chongqing University Chongqing China School of Mathematic and Information Sciences Yantai University Yantai China Department of Computer Science Georgia State University Atlanta GA USA

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

The evolution of 5G and 6G technologies has boosted mobile network speed, reduced delays, and widened coverage, empowering Mobile Crowd Sensing (MCS) to overcome surface and terrain obstacles. However, this advancement brings new hurdles for online task assignment. While most MCS methods suit surface applications, they struggle with allocation in complex environments. This paper focuses on MCS in varied settings like surface, air, and high altitude. Currently, planning-based task assignment works better for one-to-one or one-to-many scenarios, with limited options for many-to-many situations. Improving platform utility, attracting top-quality crowd workers, and enhancing task completion efficiency are vital. To tackle these challenges, the paper introduces a spatial division algorithm using 3D Voronoi diagrams for complex environments. This algorithm utilizes task coordinates to delineate assignment spaces. Additionally, it introduces a two-stage many-to-many online task assignment algorithm (MOTA) that forecasts crowd workers’ arrival probabilities and combines auction-based incentives with differential evolution algorithms. MOTA ensures efficient matching of workers and tasks within spatio-temporal constraints, balancing both parties’ interests. Finally, comparative experiments on real datasets assess the proposed MOTA algorithm’s usability and effectiveness based on overall gain, running time, task count, and assignment rate.

关键词： Crowdsourcing Three-dimensional displays Web services Prediction algorithms partitioning algorithms Sensors Delays Resource management Usability Mobile computing

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A Real-Time Vehicle Detection and Speed Estimation Using YOLO V8

A Real-Time Vehicle Detection and Speed Estimation Using YOL...

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Advances in Data Engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Karthikeya Soma Linu Shibu Meenakshi N Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

For the Intelligent Transport System (ITS) to function, vehicle classification and location must be completed quickly and precisely. It is difficult to quickly and precisely perceive and recognize vehicle sorts because of the close partition between vehicles in the city and the hindrances perspectives of the image or video picture, counting photos of vehicles. We have attempted to resolve this issue by utilizing the most recent YOLO algorithm. One of the most common uses for speed estimation of moving objects is vehicle speed estimation. It has been demonstrated that the YOLOv8 show can accurately identify moving objects in real-time. Bypassing them through a YOLOv8, vehicles on the street are freed from each video outline. The YOLO show divides the outline into sections, and each section features a vehicle on its claim. A vehicle adaptation identifier follows the outline of the vehicle. The following calculation uses the similarities in the deep highlights to identify and track the question and calculates the profundity features for each bounding box. For perspective, the vehicle's speed can be determined by balancing the pixels per meter. Finally, compare our display's measurements to those of the most advanced models available. We have also added the count of vehicles passed from a particular point. The mean average precision of the algorithm is about 54 percent. The accuracy of Yolo v8 with the deep sort algorithm is around 54 percent which is comparatively higher than the previously proposed algorithms

关键词： YOLO Vehicle detection Urban areas Estimation Streaming media Real-time systems partitioning algorithms

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Neighbor Selecting Cooperation Strategies for Distributed Learning

Neighbor Selecting Cooperation Strategies for Distributed Le...

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European Signal Processing Conference (EUSIPCO)

作者： Evangelos Georgatos Christos Mavrokefalidis Kostas Berberidis Computer Engineering and Informatics Department University of Patras Greece

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In distributed learning, a network of agents co-operate for solving a common task, like training a particular neural network. The devices usually adopt an iterative procedure with two steps, namely, they, first, perform local optimization, using, e.g., stochastic gradient descent, and, then, they exchange information among them in order to achieve consensus on the final solution. In current literature, the proposed distributed algorithms achieve consensus using averaging rules over the received information at each agent. Here, the paper departs from this paradigm and focuses on “single agent” cooperation strategies in which each agent selects a particular neighbor at each iteration and uses only that information during the local optimization step. Three selection rules are designed and it is shown that they inherit the convergence properties of commonly used averaging rules. Moreover, their effectiveness is demonstrated experimentally in classification tasks over other algorithms using well-known datasets for a wide range of scenarios, capturing factors like non-IID datasets, network size, and AI model size.

关键词： Training Performance evaluation Computer aided instruction Distance learning Signal processing algorithms Stochastic processes Signal processing Classification algorithms partitioning algorithms Optimization

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Cloud-edge-terminal Collaborative Proactive Caching and Differentiated Delivery of Heterogeneous Content for AR in Metaverse

Cloud-edge-terminal Collaborative Proactive Caching and Diff...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Siya Xu Qimeng Fu Wenjing Li Peng Yu Yang Yang Long Bai State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China UNISK (Beijing) Information Technology Co. Ltd Beijing China

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Augmented Reality (AR) applications are latency-sensitive and contain significant heterogeneous content, such as mixed static objects and interactive data. Relying solely on real-time edge caching makes it difficult to meet the latency requirements of AR, disrupting user’s immersive experience. In addition, the operator can motivate terminal caching foreground content and reduce transmission costs through device-to-device (D2D). Therefore, we proposes a cloud-edge-terminal collaborative proactive caching and differentiated delivery mechanism of heterogeneous content, which reduces service response latency and improves comprehensive revenue through efficient edge collaboration methods, accurate heterogeneous content pre-caching strategies, and differentiated delivery mechanisms. Firstly, we synthetically considers AR user’s service response latency and operator’s comprehensive revenue, proposing a user behavior and resource-aware edge collaborative service domain construction method to improve the collaborative service capability of edge nodes. Then, it proposes a pre-caching algorithm for heterogeneous content based on foreground/background content separation, user preference prediction, and storage space partitioning to improve cache utilization in the edge network. In particular, a D2D-assisted differentiated delivery strategy is designed to improve service response speed and overall revenue. The numerical results show that the proposed mechanisms are better than other solutions and can improve cache hit rates and operator’s comprehensive revenue.

关键词： Computers Costs Accuracy Metaverse Collaboration Immersive experience Prediction algorithms Real-time systems partitioning algorithms Device-to-device communication

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Research on Financial Fraud Algorithm Based on Federal Learning and Big Data Technology

Research on Financial Fraud Algorithm Based on Federal Learn...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Xinye Sha Columbia University New York United States

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

With the deepening of the digitization degree of financial business, financial fraud presents more complex and hidden characteristics, which poses a severe challenge to the risk prevention and control ability of financial institutions. At the same time, the vigorous development of big data technology provides massive potential information resources. And federated learning, as an emerging distributed machine learning paradigm, can realize multi-party data collaborative modeling under the premise of protecting data privacy. This paper firstly elaborates the basic principle, advantages and unique value of federated learning in solving data silos and protecting user privacy. Aiming at the needs of financial fraud detection, this paper discusses the design of federal learning architecture suitable for this scenario, including selecting suitable model type (such as neural network), setting reasonable data partitioning and updating rules. The central theme of the dissertation revolves around the exploration and execution of an algorithm for detecting financial fraud, which is grounded in federated learning methodologies. With a federated learning framework, each participant trains the model locally and exchanges only model parameters rather than raw data, enabling iterative optimization of the global model while protecting data privacy. To ascertain the efficacy and superiority of the suggested algorithm, a meticulous experimental investigation is both devised and executed. A real-world financial fraud dataset is selected to compare the fraud detection performance using traditional centralized learning and federated learning. Evaluation indicators include accuracy, recall rate, F1 score, etc. The findings from the experiments reveal that the federated learning-based financial fraud algorithm achieves a substantial reduction in the likelihood of data privacy breaches without compromising on high detection accuracies. Furthermore, it adeptly addresses challenges such as imba

关键词： Data privacy Accuracy Federated learning Prevention and mitigation Finance Big Data Data models Fraud partitioning algorithms Protection

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