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CNN-LSTM Implementation Methodology on SoC FPGA for Human Action Recognition Based on Video

CNN-LSTM Implementation Methodology on SoC FPGA for Human Ac...

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Euromicro symposium on Digital System Design

作者： Daniel Suárez Víctor Fernández Héctor Posadas Microelectronics Engineering Group Universidad de Cantabria Santander Spain

ISBN: (数字)9798350380385

ISBN: (纸本)9798350380392

the growing use of AI -driven video applications like surveillance or healthcare monitoring underscores the need for embedded solutions capable of accurately categorizing human actions in real-time videos. A methodology is proposed for implementing a customized CNN-LSTM architecture on AMD-Xilinx SoC FPGA devices for human action categorization from video data. In this approach, CNN operations are accelerated by the Vitis-AI DPU within the FPG A, offering flexibility to support a range of CNN architectures without requiring individual hardware description language development. this adaptability is crucial given the varying performance of CNN models across datasets. LSTM operations are executed on the SoC processors, overcoming limitations in the support provided by DPU IP cores for such networks, while maintaining flexibility to assess different configurations. Additionally, a pipeline strategy is proposed to enable parallel execution of both CNN and LSTM components, optimizing resource utilization and minimizing idle times. To demonstrate the validity of the proposed implementation methodology, experiments were conducted on the ZCUI02 de-velopment board, equipped with a Zynq Ultrascale+ MP-SoC, and involved the use of the VGG 16 CNN model along with the exploration of different LSTM configurations. the results demonstrate remarkable computational performance, achieving frame rates of up to 44.34 FPS for videos recorded at a resolution of 320×240 pixels, surpassing real-time requirements. Aditionally, the proposed implementation maintains high accuracy levels, exemplified by the single bidirectional LSTM layer achieving a competitive accuracy of 73.33% based on the UCF10l dataset.

关键词： performance evaluation Accuracy Program processors Surveillance Pipelines computer architecture Streaming media Real-time systems Resource management Field programmable gate arrays

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Hardware Acceleration of Approximate Transform Module for the Versatile Video Coding Standard

Hardware Acceleration of Approximate Transform Module for th...

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European Signal Processing Conference (EUSIPCO)

作者： Ahmed Kammoun Wassim Hamidouche Pierrick Philipp Fatma Belghith Nouri Massmoudi Jean-François Nezan Univ Rennes INSA Rennes CNRS IETRUMR France Univ Sfax ENIS LETI-LR99ES37 Tunisia

Versatile Video Coding (VVC) is the next generation video coding standard expected by the end of 2020. VVC introduces several new coding tools that enable better coding performance compared to the high Efficiency Video Coding (HEVC) standard. the Multiple Transform Selection (MTS) concept, as introduced in VVC, relies on three trigonometrical transforms, and at the encoder side, selects the couple of horizontal and vertical transforms that maximises the RateDistortion cost. However, the new Discrete Sine Transform (DST)VII and Discrete Cosine Transform (DCT)-VIII do not have fast computing algorithms and rely on matrix multiplication, which requires high hardware resources especially for large block *** paper tackles the hardware implementation of an approximation of MTS module. this approximation consists in applying adjustment stages, based on sparse block-band matrices, to a variants of DCT-II family mainly DCT-II and its inverse. therefore, an efficient 2D hardware implementation of the forward and inverse approximate transform module is proposed. the architecture design includes a pipelined and reconfigurable forward-inverse DCT-II core transform. A unified 2D implementation of 16 and 32-point forward-inverse DCTII, approximate DST-VII and DCT-VIII is also presented. the synthesis results show that the design is able to sustain 2K and 4K videos at 377 and 94 frames per second, respectively, while using only 18% of Alms, 40% of registers and 34% of Digital Signal Processing (DSP) blocks of the ArrialO SoC platform.

关键词： Discrete cosine transforms Two dimensional displays computer architecture Hardware Encoding Sparse matrices

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A multi-FPGA based platform for emulating a 100m-transistor-scale processor with high-speed peripherals (abstract only) 10

A multi-FPGA based platform for emulating a 100m-transistor-...

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Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays

作者： Huandong Wang Xiang Gao Yunji Chen Dan Tang Weiwu Hu Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781605589114

this paper describes a multi-FPGA based platform for emulating the Loongson-2G micro-processor on different mother boards. this platform is developed targeting at verification and evaluation of the Loongson-2G micro-processor, which is the next generation of Loongson-2 family, composed by one four-issue, out-of-order execution way 64-bit MIPS-compatible processor core named GS464, one 1M byte secondary Cache, one HyperTransport IO interface, one DDR2/3 memory interface and some other low speed IO interfaces. Most parts of this micro-process are mapped into the multi-FPGA based platform which consists two Vertex-5 330 FPGA chips. Semi-custom partitioning tactics within the entire design flow are developed to synthesize the whole designed into the multi-FPGA based platform. Modifications in architectural level are applied to the original architecture of the chip, in order to make it easy to be partitioned into two parts. high speed SEDES of HyperTransport IO link and DDR2/3 memory interface are emulated by using several clocks with different clock phases. To resolve the problem that hard to debug in FPGA system, a method by software probe with help of injected hardware modules in FPGA is developed and used to debug the problem causing by behavior mismatching between the ASIC ram block and the FPGA ram block. Some evaluation work on performance of Loongson-2G is done on this multi-FPGA based platform as pre-silicon test. To the authors' knowledge, there has been no previous work on such a big design used for verification and evaluation.

关键词： multi-fpga emulation loongson verification evaluation fpga

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Morphological Dilated Preprocessing with Depthwise Separable Visual Geometry Group Model for Classification of Minerals

Morphological Dilated Preprocessing with Depthwise Separable...

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Global Conference for Advancement in Technology (GCAT)

作者： M. Shyamala Devi K K Prateeka A Rishaliniirien A Reemaaasmin K Hemavathy Akshayavarthini Akshayavarthini Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamilnadu India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Accurate identification of minerals may have considerable influence on research and industrial applications, which makes it a crucial task in geology and materials science. Image classification challenges have shown significant results for traditional Convolutional Neural Networks (CNNs), such as VGG model. Unfortunately, because of their depth and variety of parameters, these CNN models frequently need large amounts of computational power. For enhancing the performance of mineral classification while preserving high accuracy, this work introduces a unique method that incorporates depth wise separable convolutions into the VGG architecture. mineral images to the current CNN models. By splitting the convolution process into depthwise and pointwise convolutions, depthwise separable convolutions are a viable substitute for constrained computing settings due to their ability to reduce both computational complexity and model size. So the traditional VGG model was intergrated with depthwise separable convolutions at the end to propose Depthwise Separable VGG16 (DS-VGG16) that uses Mineral Identification Dataset from KAGGLE for execution. the efficiency of the proposed DS-VGG model was done in by applying the morphological reconstructed Dilated mineral image throug preprocessing. the DS-VGG16 classifies six mineral types by comparing with existing CNN models on evaluating the mineral identification dataset. this paper makes a significant addition to the fields of CNN by demonstrating the potential of depthwise separable convolutions in strengthening deep learning models for mineral classification tasks. the execution shows that the proposed DS-VGG16 model performs well in classifying mineral types, with a high accuracy of 99.63%.

关键词： Geometry Visualization Materials science and technology Accuracy Filtering Computational modeling Minerals Convolutional neural networks Image reconstruction Image classification

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Multiple Bit Error Detection and Correction in Memory

Multiple Bit Error Detection and Correction in Memory

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Euromicro symposium on Digital System Design

作者： J.F. Tarillo N. Mavrogiannakis C.A. Lisboa C. Argyrides L. Carro Universidade Federal do Rio Grande do Sul Porto Alegre RS BR Department of Computer Science Bristol University UK Instituto de Informática PPGC Universidade Federal do Rio Grande do Sul Brazil Department of Computing and Electronics Oxford Brookes University UK

Technology evolution provides ever increasing density of transistors in chips, lower power consumption and higher performance. In this environment the occurrence of multiple-bit upsets (MBUs) becomes a significant concern. Critical applications need high reliability, but traditional error mitigation techniques assume only the single error model, and only a few techniques to correct MBUs at algorithm level have been proposed. In this paper, a novel circuit level technique to detect and correct multiple errors in memory is proposed. Since it is implemented at circuit level, it is transparent to programmers. this technique is based in the Decimal Hamming coding and here it is compared to Reed Solomon coding at circuit level. Experimental results show that for memory words wider than 16 bits, the proposed technique is faster and imposes lower area overhead than optimized RS, while mitigating errors affecting up to 25% of the memory word.

关键词： Decoding DH-HEMTs Polynomials Encoding Memory management Galois fields Logic gates

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Adaptive Fusion-Enhanced CNN architectures for Advanced Image Classification in E-Commerce

Adaptive Fusion-Enhanced CNN Architectures for Advanced Imag...

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International Conference on computing and Networking Technology (ICCNT)

作者： Shantanu Kumar Senior Software Engineer Amazon US

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Image classification is a fundamental aspect of modern artificial intelligence, impacting various applications ranging from autonomous vehicles to medical diagnostics. With the development of Convolutional Neural Networks (CNNs), traditional classification methods have been significantly enhanced, improving the accuracy of image analysis by capturing complex patterns within extensive datasets. Despite these advancements, a notable challenge remains in dynamically integrating multiple CNN architectures to fully utilize their collective strengths, especially in adapting efficiently to diverse image characteristics. To address this challenge, this study introduces a novel fusion model incorporating the unique features of three well-known CNNs: VGG16, ResNet50, and MobileNetV2. this model employs a dynamic concatenation strategy that effectively merges each network’s unique capabilities based on the input images’ specific characteristics, thereby improving the model’s adaptability and performance. Designed to leverage the depth and complexity of VGG16, the residual learning capabilities of ResNet50, and the efficiency of MobileNetV2, this robust system excels in accuracy and computational efficiency. Experimental results on the Fashion-MNIST dataset confirm the effectiveness of our fusion model, which achieves a training accuracy of 97.50% and a validation accuracy of 89.27%. this model markedly surpasses traditional single-model approaches, especially in distinguishing more nuanced categories, as shown by detailed evaluations including precision, recall, F1-score, and ROC curve analysis. this research effectively closes a significant gap in adaptive model fusion. It establishes a new benchmark for image classification tasks, presenting substantial implications for future applications that demand high accuracy and efficiency.

关键词： Training Adaptation models Accuracy Computational modeling Scalability computer architecture Convolutional neural networks Vehicle dynamics Image classification Residual neural networks

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Integrated Optical Sensing, Communication, and Computation System for Quadruped Robots

Integrated Optical Sensing, Communication, and Computation S...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Chen Zhu Zhouxiang Zhao Jianrong Bao Zhaohui Yang Chongwen Huang Zhaoyang Zhang School of Communication Engineering Hangzhou Dianzi University Hangzhou China Polytechnic Institute Zhejiang University Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

this paper develops an integrated optical sensing, communication, and computation system for quadruped robot with virtual-real interaction based on digital twin technology. the system is designed to address key challenges in robotics technology, including real-time performance, low-latency control, high-precision multi-sensor data fusion, stable network transmission, user-friendly interaction interface, system scalability, and maintainability. the system comprises a number of functional modules, including a three-dimensional (3D) modeling module, a positioning perception module, a virtual interaction module, a wise sensing-transmission module, and a cloud server. the 3D modeling module is responsible for constructing the virtual quadruped robot and motion space scenarios. the positioning perception module integrates LiDAR and inertial measurement unit (IMU) data, utilizing Point-light detection and ranging inertial odometry (LIO) and HDL-localization algorithms for high-precision environmental perception and positioning. the virtual interaction module provides a user-friendly control inter-face through computer software and the HoloLens headset. the wise sensing-transmission module employs WiFi and 5G links to ensure low-latency and high-bandwidth data transmission. the optical signal, sensed via camera and LiDAR, is computed at the quadruped robot and subsequently transmitted to the cloud server via the wise sensing-transmission module, which facilitates integrated optical sensing, communication, and computation. the system is designed to run on Ubuntu 20.04 platform, offering excellent scalability and maintainability.

关键词： Cloud computing three-dimensional displays Robot kinematics Robot sensing systems Real-time systems Digital twins Quadrupedal robots Servers Optical sensors Robots

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the program decision logic approach to predicated execution 99

The program decision logic approach to predicated execution

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Proceedings of the 26th annual international symposium on computer architecture

作者： David I. August John W. Sias Jean-Michel Puiatti Scott A. Mahlke Daniel A. Connors Kevin M. Crozier Wen-mei W. Hwu Center for Reliable and High-Performance Computing University of Illinois Urbana-Champaign IL Logic Systems Laboratory (DI-LSL) Swiss Federal Institute of Technology of Lausanne (EPFL) CH-1015 Lausanne Switzerland Hewlett-Packard Laboratories Hewlett-Packard Palo Alto CA

ISBN: (纸本)9780769501703

Modern compilers must expose sufficient amounts of Instruction-Level Parallelism (ILP) to achieve the promised performance increases of superscalar and VLIW processors. One of the major impediments to achieving this goal has been inefficient programmatic control flow. Historically, the compiler has translated the programmer's original control structure directly into assembly code with conditional branch instructions. Eliminating inefficiencies in handling branch instructions and exploiting ILP has been the subject of much research. However, traditional branch handling techniques cannot significantly alter the program's inherent control structure. the advent of predication as a program control representation has enabled compilers to manipulate control in a form more closely related to the underlying program logic. this work takes full advantage of the predication paradigm by abstracting the program control flow into a logical form referred to as a program decision logic network. this network is modeled as a Boolean equation and minimized using modified versions of logic synthesis techniques. After minimization, the more efficient version of the program's original control flow is re-expressed in predicated code. Furthermore, this paper proposes extensions to the HPL PlayDoh predication model in support of more effective predicate decision logic network minimization. Finally, this paper shows the ability of the mechanisms presented to overcome limits on ILP previously imposed by rigid program control structure.

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A Lightweight Convolutional Neural Network for Rice Diseases Classification

A Lightweight Convolutional Neural Network for Rice Diseases...

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IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Mohd Aniq Ismail Mohamad Rahmat Rashid Khairul Azman Ahmad Samsul Setumin Siti Juliana Abu Bakar Adi Izhar Che Ani Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Cawangan Pulau Pinang Permatang Pauh Campus Pulau Pinang Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Rice, also known as Oryza Sativa, is widely recognized as an important source of food crops and a primary source of nutrients for human beings as it is rich in carbohydrates, providing a high-energy food. the United Nations General Assembly stated that rice is the staple food of more than half the world’s population. However, pests and disease outbreaks are the major problems for farmers as this rice crop is susceptible to fungi and viruses. Leaf Brown Spot, Leaf Blast, and Leaf Hispa are among the most well-known diseases that affect the rice crop. Accurate and fast classification of these diseases is essential for implementing timely and targeted approaches to mitigate crop losses. this paper investigates the feasibility of our proposed lightweight model to be used for the classification task with a limited number of training samples. the images were preprocessed to enhance the color and edges. the K-means clustering method was used for image segmentation to separate the affected regions from the background. the proposed convolutional neural network model was made from scratch to have only three convolutional layers. the results demonstrate that the proposed model outperforms the VGG-16 model for overall performance with 90% and 85% for both batch sizes.

关键词： Training Image segmentation Accuracy Image edge detection Crops computer architecture Nutrients Convolutional neural networks Image enhancement Diseases

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the Healthgrid White Paper 3

The Healthgrid White Paper

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3rd Annual Conference of the Healthgrid Association, Healthgrid 2005

作者： Breton, Vincent Dean, Kevin Solomonides, Tony Aloisio, Giovanni Benkner, Siegfried Bilofsky, Howard Blanquer, Ignacio Brady, Michael Cannataro, Mario Chouvarda, Ioanna Claerhout, Brecht De Moor, Georges De Neve, Wilfried De Wagter, Carlos Fiore, Sandro Hassan, Kinda Heeren, Germaine Hernández, Vicente Herveg, Jean A.M. Hofmann, Martin Jones, Chris Koutkias, Vassilios Lloyd, Sharon Lonsdale, Guy Alonso, Victoria L. Maglaveras, Nicos Maigne, Lydia Malousi, Andigoni Martín-Sánchez, Fernando McClatchey, Richard Medico, Enzo Miguet, Serge Mirto, Maria Montagnat, Johan Nørager1, Sofie Nozaki, Kazunori Oliveira, Ilídio Castro Pennec, Xavier Poullet, Yves Merino, Juan Pedro Sánchez Strizh, Irina G. Taillet, Michel Tristram, Clive Tverdokhlebov, Nikolay Veltri, Pierangelo Ziegler, René CNRS-IN2P3 LPC Campus des Cézeaux AubiereCedex63177 France Internet Business Solutions Group Cisco Systems 9 New Square Bedfont Lakes Middlesex FelthamTW14 8HA United Kingdom University of the West of England Bristol CEMS Coldharbour Lane BristolBS6 6TH United Kingdom Center for Advanced Computational Technologies ISUFI Dept. of Innovation Engineering University of Lecce Via per Monteroni Lecce73100 Italy Institute of Scientific Computing University of Vienna Nordbergstrasse 15 ViennaA-1090 Austria University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Center for Bioinformatics 1416 Blockley Hall 423 Guardian Drive PhiladelphiaPA19104-6021 United States Universidad Politecnica de Valencia Camino de Vera/n Valencia46022 Spain FRS FREng Dept. of Engineering Science Oxford University Parks Road OxfordOX1 3PJ United Kingdom Magna Graecia University of Catanzaro School of Bioinformatics and Biomedical Engineering Campus di Germaneto Viale Europa Germaneto Catanzaro88100 Italy Aristotle University The Medical School Lab of Medical Informatics Box 323 Thessaloniki54124 Greece Custodix NV Verlorenbroodstraat 120 Bus 14 MerelbekeB-9820 Belgium Research in Advanced Medical Informatics and Telematics UZ Gent 5K3 De Pintelaan 185 GentB9000 Belgium Department of Radiotherapy Ghent University Hospital Building P7 De Pintelaan 185 GentB-9000 Belgium Laboratoire LIRIS Université Lumière Lyon 2 Campus Porte des Alpes 5 avenue Pierre Mendès France Bron69676 France av. E.Mounierlaan 83 Brussels1200 Belgium Faculté de Droit de Namur - FUNDP Centre de Recherches Informatiques & Droit 5 rempart de la Vierge Namur5000 Belgium Schloss Birlinghoven Sankt Augustin53754 Germany CERN Geneva 231211 Switzerland Oxford University Computing Laboratory Wolfson Building OxfordOX1 3QD United Kingdom C&C Research Laboratories NEC Europe Ltd. Rathausallee 10 Sankt AugustinD-53757 Germany Medic

ISBN: (纸本)158603510X

Over the last four years, a community of researchers working on Grid and high performance computing technologies started discussing the barriers and opportunities that grid technologies must face and exploit for the development of health-related applications. this interest lead to the first Healthgrid conference, held in Lyon, France, on January 16th–17th, 2003, with the focus of creating increased awareness about the possibilities and advantages linked to the deployment of grid technologies in health, ultimately targeting the creation of a European/international grid infrastructure for health. the topics of this conference converged with the position of the eHealth division of the European Commission, whose mandate from the Lisbon Meeting was "To develop an intelligent environment that enables ubiquitous management of citizens’ health status, and to assist health professionals in coping with some major challenges, risk management and the integration into clinical practice of advances in health knowledge." In this context "Health" involves not only clinical procedures but covers the whole range of information from molecular level (genetic and proteomic information) over cells and tissues, to the individual and finally the population level (social healthcare). Grid technology offers the opportunity to create a common working backbone for all different members of this large "health family" and will hopefully lead to an increased awareness and interoperability among disciplines. the first HealthGrid conference led to the creation of the Healthgrid association, a non-profit research association legally incorporated in France but formed from the broad community of European researchers and institutions sharing expertise in health grids. After the second Healthgrid conference, held in Clermont-Ferrand on January 29th–30th, 2004, the need for a "white paper" on the current status and prospective of health grids was raised. Over fifty experts from different areas of grid tec

关键词： Health risks

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