RECONFIGURABLE FPGA BASED SOFT-CORE PROCESSOR FOR SIMD APPLICATIONS

Objective: The prospective need of SIMD (Single Instruction and Multiple Data) applications like video and image processing in single system requires greater flexibility in computation to deliver high quality real time data. This paper performs an analysis of FPGA (Field Programmable Gate Array) based high performance Reconfigurable OpenRISC1200 (ROR) soft-core processor for SIMD.Methods: The ROR1200 ensures performance improvement by data level parallelism executing SIMD instruction simultaneously in HPRC (High Performance Reconfigurable Computing) at reduced resource utilization through RRF (Reconfigurable Register File) with multiple core functionalities. This work aims at analyzing the functionality of the reconfigurable architecture, by illustrating the implementation of two different image processing operations such as image convolution and image quality improvement. The MAC (Multiply-Accumulate) unit of ROR1200 used to perform image convolution and execution unit with HPRC is used for image quality improvement.Result: With parallel execution in multi-core, the proposed processor improves image quality by doubling the frame rate up-to 60 fps (frames per second) with peak power consumption of 400mWatt. Thus the processor gives a significant computational cost of 12ms with a refresh rate of 60Hz and 1.29ns of MAC critical path delay.Conclusion:This FPGA based processor becomes a feasible solution for portable embedded SIMD based applications which need high performance at reduced power consumptions

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A portable smartphone-based laryngoscope system for high-speed vocal cord imaging of patients with throat disorders (Preprint)

10.2196/preprints.25816 ◽

2020 ◽

Author(s):

Jun Ki Kim ◽

Youngkyu Kim ◽

Jungmin Oh ◽

Seung-Ho Choi ◽

Ahra Jung ◽

...

Keyword(s):

Image Processing ◽

Vocal Cord ◽

High Speed ◽

High Performance ◽

Vocal Folds ◽

Frame Rate ◽

Endoscopic Imaging ◽

High Speed Imaging ◽

Underdeveloped Countries ◽

Longitudinal Edge

BACKGROUND Recently, high-speed digital imaging (HSDI), especially HSD endoscopic imaging is being routinely used for the diagnosis of vocal fold disorders. However, high-speed digital endoscopic imaging devices are usually large and costly, which limits access by patients in underdeveloped countries and in regions with inadequate medical infrastructure. Modern smartphones have sufficient functionality to process the complex calculations that are required for processing high-resolution images and videos with a high frame rate. Recently, several attempts have been made to integrate medical endoscopes with smartphones to make them more accessible to underdeveloped countries. OBJECTIVE To develop a smartphone adaptor for endoscopes to reduce the cost of devices, and to demonstrate the possibility of high-speed vocal cord imaging using the high-speed imaging functions of a high-performance smartphone camera. METHODS A customized smartphone adaptor was designed for clinical endoscopy using selective laser melting (SLM)-based 3D printing. Existing laryngoscope was attached to the smartphone adaptor to acquire high-speed vocal cord endoscopic images. Only existing basic functions of the smartphone camera were used for HSDI of the vocal folds. For image processing, segmented glottal areas were calculated from whole HSDI frames, and characteristics such as volume, shape and longitudinal edge length were analyzed. RESULTS High-speed digital smartphone imaging with the smartphone-endoscope adaptor could achieve 940 frames per second, and was used to image the vocal folds of five volunteers. The image processing and analytics demonstrated successful calculation of relevant diagnostic variables from the acquired images. CONCLUSIONS A smartphone-based HSDI endoscope system can function as a point-of-care clinical diagnostic device. Furthermore, this system is suitable for use as an accessible diagnostic method in underdeveloped areas with inadequate medical service infrastructure.

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Research on Space System with ACP Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.2387 ◽

2013 ◽

Vol 380-384 ◽

pp. 2387-2390 ◽

Cited By ~ 1

Author(s):

He Ran Tang ◽

Zhi Li

Keyword(s):

High Performance ◽

Real Space ◽

Research Field ◽

Parallel Execution ◽

Space System ◽

Time Data ◽

Artificial System ◽

Agent Based ◽

Special Unit ◽

High Performance Computer

The ACP (artificial societies, computational experiments, and parallel execution) method, derived from parallel systems theory, belongs to the research field of meta-synthesis. During the past 10 years after it was proposed, ACP has been applied in transportation, agriculture and economics, demonstrating its advantages. In this paper, ACP is initially proposed to apply in research of space system in the hope that an artificial system can be built for tests, thereby providing guide to construction of real space system. Specifically, modeling of special unit is conducted with the Agent-based approach; high-performance computer is provided for computational experiment; and finally the real-time data published on web is used for parallel execution.

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A FPGA-BASED PARAMETRIZABLE SYSTEM FOR HIGH-RESOLUTION FREQUENCY-DOMAIN IMAGE FILTERING

Journal of Circuits System and Computers ◽

10.1142/s0218126605002775 ◽

2005 ◽

Vol 14 (05) ◽

pp. 895-921

Author(s):

ISA SERVAN UZUN ◽

ABBES AMIRA

Keyword(s):

Image Processing ◽

Frequency Domain ◽

High Performance ◽

Fourier Transforms ◽

Processing System ◽

Image Filtering ◽

Input Image ◽

Frame Rate ◽

Detailed Knowledge ◽

Image Size

Signal and image processing applications require high computational power with the ability to experiment different algorithms involving matrix transforms. Reconfigurable hardware devices in the form of Field Programmable Gate Arrays (FPGAs) have been proposed to obtain high performance at an economical price. However, the users must program FPGAs at a very low level and must have a detailed knowledge of the architecture of the device being used. In trying to reconcile the dual requirements of high performance and the ease of development, this paper reports the design and realization of the Fast Fourier Transforms (FFTs) using a FPGA-based environment, which enables system designer to meet different system requirements (i.e., chip area, speed, memory, etc.) for a range of signal processing and imaging applications. The use of the proposed environment has been proven by the developing a high-level FPGA-based parametrizable image processing system for frequency-domain filtering application. The system achieves real-time image filtering performance exceeding those of currently available solutions by an order of magnitude in frame rate and input image size.

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Model-Based Parallelization for Simulink Models on Multicore CPUs and GPUs

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v20i.8533 ◽

2020 ◽

Vol 20 ◽

pp. 1-13

Author(s):

Zhaoqian Zhong ◽

Masato Edahiro

Keyword(s):

Image Processing ◽

Critical Path ◽

Directed Acyclic Graphs ◽

Parallel Execution ◽

Communication Behaviors ◽

Model Based ◽

Simulink Model ◽

Acyclic Graphs ◽

Block Level ◽

Multicore Cpus

In this paper we propose a model-based approach to parallelize Simulink models of image processing algorithms on homogeneous multicore CPUs and NVIDIA GPUs at the block level and generate CUDA C codes for parallel execution on the target hardware. In the proposed approach, the Simulink models are converted to directed acyclic graphs (DAGs) based on their block diagrams, wherein the nodes represent tasks of grouped blocks or subsystems in the model and the edges represent the communication behaviors between blocks. Next, a path analysis is conducted on the DAGs to extract all execution paths and calculate their respective lengths, which comprises the execution times of tasks and the communication times of edges on the path. Then, an integer linear programming (ILP) formulation is used to minimize the length of the critical path of the DAG, which represents the execution time of the Simulink model. The ILP formulation also balances workloads on each CPU core for optimized hardware utilization. We parallelized image processing models on a platform of two homogeneous CPU cores and two GPUs with our approach and observed a speedup performance between 8.78x and 15.71x.

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Precise and parallel segmentation model (PPSM) via MCET using hybrid distributions

Applied Computing and Informatics ◽

10.1108/aci-11-2020-0123 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Soha Rawas ◽

Ali El-Zaart

Keyword(s):

Image Processing ◽

Traffic Control ◽

High Performance ◽

Health Care Systems ◽

Computational Cost ◽

Threshold Value ◽

Surveillance Systems ◽

Processing Application ◽

Content Type ◽

Gamma Distributions

PurposeImage segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.Design/methodology/approachThe proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.FindingsOn the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.Originality/valueA novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

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Matlab and Parallel Computing

Image Processing & Communications ◽

10.2478/v10248-012-0048-5 ◽

2012 ◽

Vol 17 (4) ◽

pp. 207-216 ◽

Cited By ~ 5

Author(s):

Magdalena Szymczyk ◽

Piotr Szymczyk

Keyword(s):

Image Processing ◽

Signal Processing ◽

Parallel Computing ◽

Distributed Computing ◽

Control Systems ◽

High Performance ◽

Parallel Applications ◽

Process Simulations ◽

Key Features ◽

Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

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