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A parallel Multibit programing Scheme With High Precision for RRAM-Based Neuromorphic Systems

IEEE TRANSACTIONS ON ELECTRON DEVICES 2020年第5期67卷 2213-2217页

作者： Chen, Junren Wu, Huaqiang Gao, Bin Tang, Jianshi Hu, Xiaobo Sharon Qian, He Tsinghua Univ Innovat Ctr Future Chips ICFC Inst Microelect Beijing 100084 Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

Resistive random access memory (RRAM) has been extensively studied as a promising candidate for neuromorphic computing. So far, high-precision multibit programing of RRAM for neuromorphic systems is done cell-by-cell, which can be very time-consuming. This brief demonstrates a row-by-row parallel program-verify scheme on a fabricated 160-Kb RRAM array using the incremental gate voltage programing (IGVP) method. Statistical analysis indicates that the optimal conductance tuning step size can accelerate the programing process and improve the success rate. Also, for the first time, the fundamental relations between programing success rate, critical path, and parallel efficiency for parallel program-verify methodology on the RRAM array are systematically studied. Moreover, a software-comparable recognition accuracy with only <0.3% accuracy loss is experimentally achieved for MNIST data set classification utilizing the proposed parallel program-verify scheme.

关键词： 1-transistor-1-RRAM(1T1R) array multibit neuromorphic system parallel programing program-verify resistive random access memory (RRAM)

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Bilevel and parallel programing-Based Operability Approaches for Process Intensification and Modularity

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AICHE JOURNAL 2018年第8期64卷 3042-3054页

作者： Carrasco, Juan C. Lima, Fernando V. West Virginia Univ Dept Chem & Biomed Engn Morgantown WV 26506 USA

Process operability emerged in the last decades as a powerful tool for the design and control of chemical processes. Recent efforts in operability have been focused on the calculation of the desired input set for process design and intensification of natural gas utilization applications described by nonlinear models. However, there is still a gap in terms of problem dimensionality that nonlinear operability methods can handle. To fill this gap, in this article, the incorporation of bilevel and parallel programing approaches into classical process operability concepts is discussed. Results on the implementation of the proposed method show a reduction in computational time up to two orders of magnitude, when compared to the original results without parallelization. These results could be extrapolated for use in a supercomputer as presented in the computational time analysis performed. In terms of intensification, the proposed approach can produce a natural gas combined cycle plant modular design with a dramatic reduction in size, from the original 400 to 0.11 MW, while still keeping the high net plant efficiency. This approach thus provides a computationally efficient framework for process intensification of high-dimensional nonlinear energy systems toward modularity. The proposed approach also enables the verification of a modular design and conditions that can be obtained according to economic and physical constraints associated with a specific natural gas well production. (C) 2018 American Institute of Chemical Engineers

关键词： operability bilevel optimization parallel programing process intensification modularity

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Implementation of the image super-resolution DWT based algorithm on Raspberry Pi platform for real-time applications

Implementation of the image super-resolution DWT based algor...

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Conference on Real-Time Processing of Image, Depth, and Video Information

作者： Osorno-Ortiz, Raul J. Ponomaryov, Volodymyr, I Reyes-Reyes, Rogelio Cruz-Ramos, Clara Garcia-Salgado, Beatriz P. Sadovnychiy, Sergiy Inst Politecn Nacl ESIME Culhuacan Ciudad De Mexico Mexico Inst Mexicano Petr Ciudad De Mexico Mexico

ISBN: (纸本)9781510673199;9781510673182

Super resolution (SR) is a technique designed for increasing the spatial resolution in an image from a low resolution (LR) to high resolution (HR) size. SR technology has had a considerable demand in a wide variety of applications to recover HR images, such as medicine, engineering, computer vision, pattern recognition and video production, etc. In contrast to interpolation-based algorithms that often introduce distortions or irregular borders, this study proposes an implementation that can preserve the edges and fine details of an original image through the computation of the wavelet decomposition. Different Discrete Wavelet Transform (DWT) families such as: Daubechies, Symlet, and Coiflet were evaluated. The proposed system was implemented on a Raspberry Pi 4 model B, an embedded device, to get around the PC's mobility limitations, making it possible to create an in-expensive and energy- efficient SR system, reducing their complexity in realtime applications. To investigate the visual performance, SR images have been analysed in subjective matter via human perception view, guaranteeing good perception for the images of different nature from three different datasets such as FullHD (DIV2K), medical (Raabin WBC), and remote sensing (Sentinel- 1). The experimental results of designed implementations appear to demonstrate good performance in commonly used objective criteria: execution time, SSIM, and PSNR (0.742 sec., 0.9164, and 38.72 dB), respectively for images with a super resolution size of 1356 x 2040 pixels.

关键词： Super resolution Discrete Wavelet Transform (DWT) parallel programing Raspberry Pi real-time processing

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PESC - parallel Experience for Sequential Code

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第12-14期37卷

作者： Santos, Henrique C. T. de Souza, Luciano S. de Carvalho, Jonathan H. A. Ferreira, Tiago A. E. Inst Fed Pernambuco Recife PE Brazil Univ Fed Rural Pernambuco Recife PE Brazil Univ Fed Pernambuco Recife PE Brazil

The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. Sequential codes need to be converted to parallel versions to optimize the use of these resources. Maintaining a local infrastructure for the execution of distributed computing, through desktop grids, for example, has been replaced in favor of cloud platforms that abstract the complexity of these local infrastructures. Unfortunately, the cost of accessing these resources could leave out various studies that could be carried by a simpler infrastructure. In this article, we present a platform for distributing computer simulations on resources available on a local network using container virtualization that abstracts the complexity needed to configure these execution environments and allows any user can benefit from this infrastructure. Simulations could be developed in any programming language (such as Python, Java, C, and R) and with specific execution needs within reach of the scientific community in a general way. We will present results obtained in running simulations that required more than 1000 runs with different initial parameters and various other experiments that benefited from using the platform.

关键词： container virtualization desktop grid distributed computing parallel programing sequential code

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Classification Of Human Facial Expressions For Emotion Recognition Using A Distributed Computer System 5

Classification Of Human Facial Expressions For Emotion Recog...

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5th International Conference on Computer Science and Engineering (UBMK)

作者： Korkmaz, Timucin Erol, Hamza Mersin Univ Grad Sch Nat & Appl Sci Dept Comp Engn Mersin Turkey Mersin Univ Engn Fac Dept Comp Engn Mersin Turkey

ISBN: (纸本)9781728175652

A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image files were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed arc taken into account;and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.

关键词： Big data Distributed System Emotion analysis Image classification parallel programing Face expression recognition

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Approximation Algorithm for Shortest Path in Large Social Networks

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ALGORITHMS 2020年第2期13卷 36页

作者： Mensah, Dennis Nii Ayeh Gao, Hui Yang, Liang Wei Univ Elect Sci & Technol China Big Data Res Ctr Chengdu 610051 Peoples R China

Proposed algorithms for calculating the shortest paths such as Dijikstra and Flowd-Warshall's algorithms are limited to small networks due to computational complexity and cost. We propose an efficient and a more accurate approximation algorithm that is applicable to large scale networks. Our algorithm iteratively constructs levels of hierarchical networks by a node condensing procedure to construct hierarchical graphs until threshold. The shortest paths between nodes in the original network are approximated by considering their corresponding shortest paths in the highest hierarchy. Experiments on real life data show that our algorithm records high efficiency and accuracy compared with other algorithms.

关键词： parallel programing complex networks hierarchical networks approximation algorithm shortest path

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A robust framework for tamper detection in digital recorded voice signals

A robust framework for tamper detection in digital recorded ...

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Conference on Real-Time Image Processing and Deep Learning

作者： Bernal-Patino, Adriana Ponomaryov, Volodymyr, I Reyes-Reyes, Rogelio Cruz-Ramos, Clara Inst Politecn Nacl ESIME Culhuacan Ciudad De Mexico Mexico

ISBN: (纸本)9781510626584

This paper focuses on the tampering detection and pinpointing the manipulated region in digital recorded voice signals via blind semi-fragile watermark embedded in the frequency domain using the Fast Fourier Transform and Quantization Index Modulation-Dither Modulation. The proposed method has been evaluated with several male and female recorded voice signals. The designed method can provide non-audible perceptible difference between the original and the watermarked voice signals obtaining an average Signal-to-Noise Ratio of 44dB, providing resistance to various MP3 compression rates. The simulation results of the parallel implementation of the proposed technique on multicore central processing unit platform has shown efficient performance in a real-time environment.

关键词： Speech watermarking Tamper detection Inaudibility Quantization Index Modulation-Dither Modulation Multi-core central processing unit parallel programing

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On the Evaluation of Different High-Performance Computing Platforms for Hyperspectral Imaging: An OpenCL-Based Approach

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第11期10卷 4879-4897页

作者： Guerra, Raul Martel, Ernestina Khan, Jehandad Lopez, Sebastian Athanas, Peter Sarmiento, Roberto Inst Appl Microelect Integrated Syst Design Div Las Palmas Gran Canaria 35017 Spain Univ Las Palmas Gran Canaria Inst Univ Microelect Aplicada Las Palmas Gran Canaria 35017 Spain Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA

Hyperspectral imaging systems are a powerful tool for obtaining surface information in many different spectral channels that can be used in many different applications. Nevertheless, the huge amount of information provided by hyperspectral images also has a downside, since it has to be processed and analyzed. For such purpose, parallel hardware devices, such as field-programmable gate arrays (FPGAs) and graphic processing units (GPUs), are typically used, especially for hyperspectral imaging applications under real-time constraints. However, developing hardware applications typically requires expertise in the specific targeted device, as well as in the tools and methodologies that can be used to perform the implementation of the desired algorithms in that device. In this scenario, the Open Computing Language (OpenCL) emerges as a very interesting solution in which a single high-level language can be used to efficiently develop applications in multiple and different hardware devices. In this work, the parallel Fast Algorithm for Linearly Unmixing Hyperspectral Images (pFUN) has been implemented in two different NVIDIA GPUs, the GeForce GTX 980 and the Tesla K40c, using OpenCL. The obtained results are compared with the results provided by the previously developed NVIDIA CUDA implementation of the pFUN algorithm for the same GPU devices for comparing the efficiency of OpenCL against a more specific synthesis design language for the targeted hardware devices, such as CUDA is for NVIDIA GPUs. Moreover, the FUN algorithm has also been implemented into a Bitware Stratix V Altera FPGA, using OpenCL, for comparing the results that can be obtained using OpenCL when targeting different devices and architectures. The obtained results demonstrate the suitability of the followed methodology in the sense that it allows the achievement of efficient FPGA and GPU implementations able to cope with the stringent requirements imposed by hyperspectral imaging systems.

关键词： CUDA field-programmable gate array (FPGA) graphic processing unit (GPU) high-performance computing hyperspectral imaging hyperspectral unmixing open computing language (openCL) parallel programing

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parallel Steganography Framework for Hiding a Color Image Inside Stereo Images

Parallel Steganography Framework for Hiding a Color Image In...

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Conference on Real-Time Image and Video Processing

作者： Munoz-Ramirez, David O. Ponomaryov, Volodymyr I. Reyes-Reyes, Rogelio Cruz-Ramos, Clara Inst Politecn Nacl ESIME Culhuacan Av Santa Ana 1000 Mexico City 04430 DF Mexico

ISBN: (纸本)9781510609471;9781510609488

In this work, a robust steganography framework to hide a color image into a stereo images is proposed. The embedding algorithm is performed via Discrete Cosine Transform (DCT) and Quantization Index Modulation-Dither Modulation (QIM-DM) hiding the secret data. Additionally, the Arnold's Cat Map Transform is applied in order to scramble the secret color image, guaranteeing better security and robustness of the proposed system. Novel framework has demonstrated better performance against JPEG compression attacks among other existing approaches. Besides, the proposed algorithm is developed taking into account the parallel paradigm in order to be implemented in multi-core CPU increasing the processing speed. The results obtained by the proposed framework show high values of PSNR and SSIM, which demonstrate imperceptibility and sufficient robustness against JPEG compression attacks.

关键词： Steganography Digital Color Image Stereo Images 3D Multi-core CPU parallel programing C#

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Performance Analysis of VNFs for Sizing Cloud-RAN Infrastructures

Performance Analysis of VNFs for Sizing Cloud-RAN Infrastruc...

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IEEE Conference Network Function Virtualization and Software Defined Networks (NFV-SDN)

作者： Rodriguez, Veronica Quintuna Guillemin, Fabrice Orange Labs 2 Ave Pierre Marzin F-22300 Lannion France

ISBN: (纸本)9781538632857

We explore in this paper the design of a Cloud-RAN architecture. We study the problem of determining the required computing capacity for hosting software-based Base Band Units (BBUs) of various eNodeBs in a data center. A worst-case analysis in terms of execution time of BBU functions is then performed. We specifically investigate how to reduce their runtime and thus to increase the concentration level of these functions in the back-haul network. For this purpose, we explore how to execute on a multi-core platform encoding and decoding tasks, which are the most greedy in terms of processing time. By introducing various parallelism schemes, we quantify the gain in latency. It turns out that parallelism on a Code Block (CB) basis is the most efficient as soon as the computing capacity of the multi-core platform is above a certain threshold. The gain is then substantial and scales with the processing capacity, allowing higher concentration of eNodeBs in the BBU-pool.

关键词： Cloud-RAN NFV VNF channel coding parallel programing resource pooling global scheduler

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