Beamforming complexity reduction methods for low-cost FPGA-based implementation

Siritan, T.; Techavipoo, Udomchai; Worasawate, Denchai; Keinprasit, Rachaporn; Pinunsottikul, P.; Sugino, Nobuhiko; Thajchayapong, P.

doi:10.1109/bmeicon.2013.6687642

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…To solve this problem, a higher performance GPU is needed, e.g., a GPU with higher numbers of CUDA cores, such as 1536 or 3072 cores, which are already available in modern graphics cards. Rather than increase the CUDA cores, reducing the beamform dynamic focusing points [12] or applying pseudo-dynamic beamforming [13] could be an economic solution.…”

Section: Discussionmentioning

confidence: 99%

“…The number of focal points is also reduced to save the FPGA memory, e.g., instead of focusing on every point on the scanline, the scanline is divided into windows, and each window has only one focal point. Moreover, the size of the windows also increases along the depth to further reduce the focal points [12]. Because of using one focal point per window, other points in that window are out of focus.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Toward Optimal Computation of Ultrasound Image Reconstruction Using CPU and GPU

Techavipoo

Worasawate

Boonleelakul

et al. 2016

Sensors

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An ultrasound image is reconstructed from echo signals received by array elements of a transducer. The time of flight of the echo depends on the distance between the focus to the array elements. The received echo signals have to be delayed to make their wave fronts and phase coherent before summing the signals. In digital beamforming, the delays are not always located at the sampled points. Generally, the values of the delayed signals are estimated by the values of the nearest samples. This method is fast and easy, however inaccurate. There are other methods available for increasing the accuracy of the delayed signals and, consequently, the quality of the beamformed signals; for example, the in-phase (I)/quadrature (Q) interpolation, which is more time consuming but provides more accurate values than the nearest samples. This paper compares the signals after dynamic receive beamforming, in which the echo signals are delayed using two methods, the nearest sample method and the I/Q interpolation method. The comparisons of the visual qualities of the reconstructed images and the qualities of the beamformed signals are reported. Moreover, the computational speeds of these methods are also optimized by reorganizing the data processing flow and by applying the graphics processing unit (GPU). The use of single and double precision floating-point formats of the intermediate data is also considered. The speeds with and without these optimizations are also compared.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Optimal Computation of Ultrasound Image Reconstruction Using CPU and GPU

Techavipoo

Worasawate

Boonleelakul

et al. 2016

Sensors

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show abstract

“…This flaw is addressed by the NLMS algorithm. In [10], the authors proposed two approaches for simplifying dynamic receive beamforming so that it may be performed on a single 500K gate FPGA. The first technique employs a fixed window size, whereas the second utilises a dynamic window size that equalises off-focus beamforming delay errors between windows at various depths.…”

Section: Literature Surveymentioning

confidence: 99%

Computational Challenges in Firmware Implementation of Beamforming Techniques and Enhancement

Althaf

Ray

Nethravathi³

et al. 2022

Advances in Transdisciplinary Engineering

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Adaptive beamforming has been studied extensively from a simulation point of view. While existing works compare various techniques based on their simulation output performance, their emulation on hardware systems and the prerequisite analysis of firmware viability remain relatively unexplored. The work presented in this paper addresses two issues. One is the firmware implementation of adaptive beamforming and the analysis of the Hardware Description Language implementation of the Least Mean Squares (LMS) Algorithm. It begins with the development of the algorithm on MATLAB Simulation Environment and proceeds to the synthesis of Verilog code for the same on Xilinx’s Vivado platform. The optimization of the LMS algorithm has been presented as a detailed case study for the successful reduction of the number of hardware resources utilized by a synthesized design on a target Artix-7 FPGA board.

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“…Podemos optar entre muchas formas de composición de imagen en función de cómo se realice la insonificación, la adquisición, o cómo se combinen las señales recogidas (Szabo, 2004). El problema clásico de conformación de haces en recepción y tiempo real para un array de N elementos e I puntos en la imagen tiene un nivel de complejidad O(N × I), y a día de hoy se han desarrollado distintos tipos de soluciones tanto a nivel de hardware (Siritan and et al, 2013;Wall and Lockwood, 2005;Camacho and et al, 2007), como de software (So et al, 2011).…”

Section: Introductionunclassified

Análisis de la implementación software de un conformador de señales ultrasónicas para tiempo real

Romero-Laorden

Villazón-Terrazas

Santos

et al. 2016

Revista Iberoamericana de Automática e Informática Industrial R

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Este trabajo analiza la implementación software en un sistema de imagen ultrasónica del Total Focusing Method para la compensación dinámica en tiempo real de los tiempos de vuelo para emisión y recepción de todos los puntos de la imagen. Para ello, haciendo uso de técnicas GPGPU, se analizan dos diferentes alternativas de implementación, mostrando como una planificación adecuada de acceso a los datos permite mejorar los tiempos de ejecución del algoritmo.

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Beamforming complexity reduction methods for low-cost FPGA-based implementation

Cited by 7 publications

References 9 publications

Toward Optimal Computation of Ultrasound Image Reconstruction Using CPU and GPU

Toward Optimal Computation of Ultrasound Image Reconstruction Using CPU and GPU

Computational Challenges in Firmware Implementation of Beamforming Techniques and Enhancement

Análisis de la implementación software de un conformador de señales ultrasónicas para tiempo real

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