面向空间计算的带有大量微型芯片的卫星服务器设计

Mengwei Xu, Li Zhang, Hongyu Li, Ruolin Xing, Qibo Sun
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引用次数: 1

摘要

随着卫星产生的数据量的爆炸式增长,为了充分利用这些数据的价值,空间计算变得必不可少。与当今地面上的计算范例类似,太空计算成功的关键一步是在太空中构建由许多服务器机器组成的分布式微型数据中心。在这项工作中,我们提出了一种新的服务器架构,该架构由大量低功耗的片上系统(soc)组成。通过定量分析,我们确认这样的服务器在空间计算的三个关键指标上明显优于传统服务器(英特尔CPU, NVIDIA GPU等),即能效,重量和体积。值得注意的是,我们还证明了上述三个相关指标可以减少到只有一个(能源效率),因为伴随的太阳能电池板提供所需的能量,通常在重量和体积上超过服务器本身。我们进一步深入研究空间计算的两个特定应用:视频处理和深度学习推理。通过最先进的软件和基准测试,我们发现基于soc的服务器,特别是其异构处理器(GPU, DSP和MediaCodec),与传统服务器相比,可以显着提高能源效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Satellite-Born Server Design with Massive Tiny Chips Towards In-Space Computing
With the explosive data volume generated by satellites, in-space computing becomes indispensable to fully exploit the value of such data. Similar to today’s computing paradigms on the ground, a key step to the success of in-space computing is building distributed, micro data centers in space, which consist of many server machines. In this work, we propose a novel server architecture that is comprised of massive, low-power system-on-chips (SoCs). Through quantitative analysis, we confirm that such a server significantly outperforms conventional servers (Intel CPU, NVIDIA GPU, etc) on three critical metrics of in-space computing, i.e., energy efficiency, weight, and volume. Notably, we also demonstrate that the three concerned metrics above can be reduced to only one (energy efficiency) as the accompanied solar panels to provide the needed energy often overwhelm the server itself in weight and volume. We further dive into two specific applications that are representative of in-space computing: video processing and deep learning inference. Through state-of-the-art software and benchmarks, we reveal that our SoC-based server, especially with its heterogeneous processors (GPU, DSP, and MediaCodec), can remarkably improve the energy efficiency compared to conventional servers.
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