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Power vs. Performance Evaluation of Synthetic Aperture Radar Image-Formation Algorithms and Implementations for Embedded HEC Environments (Ongoing Study)

机译:嵌入式HEC环境的合成孔径雷达图像形成算法和实现的功率与性能评估(正在进行的研究)

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摘要

The continuing miniaturization and parallelization of processing hardware has facilitated the development of mobile and field-deployable systems that can accommodate terascale processing within once prohibitively small size and weight constraints. Unfortunately, the added computational capability of these small systems often comes at the cost of larger power demands, an already strained resource in these embedded systems. This study explores the power issues in a specific type of field-deployable system, Mobile Radar. Specifically, we focus on a computationally intensive phase of Synthetic Aperture Radar, Image Formation (IF), and evaluate performance tradeoffs in terms of time-to-solution, output image quality, and power consumption for two different implementations, single- and double-precision, of two different IF algorithms, one frequency-domain based and the other time-domain based. Preliminary results show that in some CPU-based instances single-precision IF leads to significant reductions in time-to-solution and, thus, total energy consumption (over 50%) with negligible but possibly acceptable SAR image output degradation. In the near future, this ongoing study will reevaluate these results, i.e., SAR IF power consumption vs. performance tradeoffs with more sophisticated IF workloads and output quality metrics, finer-grain performance and power measurement methodologies, and more computationally powerful embedded HEC devices, i.e., GPGPUs.
机译:处理硬件的持续小型化和并行化促进了移动和可现场部署系统的开发,这些系统可以在很小的尺寸和重量限制内适应万亿级处理。不幸的是,这些小型系统增加的计算能力通常是以更大的功率需求为代价的,而这些功率是这些嵌入式系统中已经紧张的资源。这项研究探讨了特定类型的可现场部署的系统Mobile Radar中的电源问题。具体来说,我们专注于合成孔径雷达的计算密集型阶段,即图像形成(IF),并针对两种不同的实现方式(单次和两次执行)评估了解决时间,输出图像质量和功耗方面的性能折衷两种不同的IF算法的精度,一种基于频域,另一种基于时域。初步结果表明,在某些基于CPU的情况下,单精度IF可以显着缩短解决时间,并因此减少了总的能量消耗(超过50%),而SAR图像输出的劣化可以忽略不计,甚至可以接受。在不久的将来,这项正在进行的研究将重新评估这些结果,即SAR IF功耗与性能之间的权衡,以及更复杂的IF工作负载和输出质量指标,更细粒度的性能和功率测量方法,以及计算能力更强的嵌入式HEC设备,即GPGPU。

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