Accelerating SAR imaging using vector extension on multi-core SIMD CPU

Li, Guojun; Zhang, Fan; Ma, Lixiang; Hu, Wei

doi:10.1109/igarss.2015.7325819

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…Vector extension model can optimize program with data-level parallelism [ 37 ]. The SIMD CPUs extend the vector extension model with the introduction of the SSE and AVX [ 33 ]. According to the existing literature, one of the important trends in technologies and computing platforms is that vectors are playing an increasingly important role in both memory accesses and arithmetic operations [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…The combination of AVX and multi-core parallel can make the computing capability of CPU comparable with GPU. Our previous works [ 31 , 32 , 33 ] have a preliminary discussion on this, and proved that CPU had competitive computing power. In terms of that, CPU no longer just takes on some auxiliary tasks of SAR imaging processing, but can really become more involved in the computing works of imaging processing.…”

Section: Introductionmentioning

confidence: 90%

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Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Zhang

et al. 2016

Sensors

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With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Zhang

et al. 2016

Sensors

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“…Several real-time processing approaches have addressed the issue of accelerating SAR algorithms using field programmable gate arrays (FPGAs) [9], digital signal processors (DSPs) [10], central processing units (CPUs) [11] and graphics processing units (GPUs) [12]. Le et al [9] introduced an FPGA design that processes the SAR image using a Range-Doppler Algorithm (RDA) [4]- [6], which due to its use of approximations, is less accurate and less robust towards higher squint angles if compared to the proposed EOK algorithm [8].…”

Section: Related Workmentioning

confidence: 99%

Resource-Constrained Optimizations For Synthetic Aperture Radar On-Board Image Processing

Schlemon

Schulz

Scheiber

2022

2022 IEEE High Performance Extreme Computing Conference (HPEC)

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Synthetic Aperture Radar (SAR) can be used to create realistic and high-resolution 2D or 3D reconstructions of landscapes. The data capture is typically deployed using radar instruments in specially equipped, low flying planes, resulting in a large amount of raw data, which needs to be processed for image reconstruction. However, due to limited on-board processing capacities on the plane (power, size, weight, cooling, communication bandwidth to ground stations, etc.) and the need to capture many images during a single flight, the raw data must be processed on-board and then sent to the ground station efficiently as image products. In this paper we describe the processing architecture of the digital beamforming SAR (DBFSAR) of the German Areaospace Center (DLR) and the special steps that had to be taken to enable the on-board processing. We explain the required software optimizations and under which conditions their integration in the SAR imaging process leads to (near) real-time capability. We further describe the lessons learned in our work and discuss how they can be applied to other processing scenarios with limited resource availability.

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“…Digital signal processors (DSPs), CPUs, and graphics processing units (GPUs) have respective advantages in real-time SAR processing. As the system adopts CPU, it has good flexibility and portability [12]. However, their power efficiency for computing is quite low, which is a bottleneck in real-time SAR applications.…”

Section: Related Workmentioning

confidence: 99%

A Highly Efficient Heterogeneous Processor for SAR Imaging

Wang

Zhang

Huang

et al. 2019

Sensors

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The expansion and improvement of synthetic aperture radar (SAR) technology have greatly enhanced its practicality. SAR imaging requires real-time processing with limited power consumption for large input images. Designing a specific heterogeneous array processor is an effective approach to meet the power consumption constraints and real-time processing requirements of an application system. In this paper, taking a commonly used algorithm for SAR imaging—the chirp scaling algorithm (CSA)—as an example, the characteristics of each calculation stage in the SAR imaging process is analyzed, and the data flow model of SAR imaging is extracted. A heterogeneous array architecture for SAR imaging that effectively supports Fast Fourier Transformation/Inverse Fast Fourier Transform (FFT/IFFT) and phase compensation operations is proposed. First, a heterogeneous array architecture consisting of fixed-point PE units and floating-point FPE units, which are respectively proposed for the FFT/IFFT and phase compensation operations, increasing energy efficiency by 50% compared with the architecture using floating-point units. Second, data cross-placement and simultaneous access strategies are proposed to support the intra-block parallel processing of SAR block imaging, achieving up to 115.2 GOPS throughput. Third, a resource management strategy for heterogeneous computing arrays is designed, which supports the pipeline processing of FFT/IFFT and phase compensation operation, improving PE utilization by a factor of 1.82 and increasing energy efficiency by a factor of 1.5. Implemented in 65-nm technology, the experimental results show that the processor can achieve energy efficiency of up to 254 GOPS/W. The imaging fidelity and accuracy of the proposed processor were verified by evaluating the image quality of the actual scene.

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Accelerating SAR imaging using vector extension on multi-core SIMD CPU

Cited by 7 publications

References 9 publications

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Resource-Constrained Optimizations For Synthetic Aperture Radar On-Board Image Processing

A Highly Efficient Heterogeneous Processor for SAR Imaging

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