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Implementation of Machine Learning Methods on FPGA for On-board Spacecraft Operation

机译：用于车载航天器运行的FPGA机器学习方法的实施

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Machine Learning (ML) techniques are increasingly being used in terrestrial applications. They have also been proposed for various space applications such as on-board data processing, planetary explorations, autonomous operations and various mission-specific applications. Terrestrial application of ML is facilitated by shared resources such as cloud services, powerful desktop computers and framework APIs. However, these enablers are limited in space environment. To take advantage of the progress in ML, space applications need to incorporate on-board inference. The first part of this paper presents a review on some space applications in which such algorithms are suitable or have been used. The review scope is limited to on-board and in-flight applications that have already been realized and doesn't factor in majority of those that are at a proposal stage. ML techniques at ground stations for analysing and processing data is not covered. The review also presents some hardware that has been or can be utilized in these implementations. Though there has been a plethora of hardware platforms geared towards ML inference on the edge, this review notes that Field Programmable Gate Arrays (FPGAs) and associated Systems-on-Chip (SoCs) are more suitable for ML inference in space applications. They also have flight-heritage, having been applied in mission-specific and satellite subsystem operations. They are also reconfigurable and can therefore be adopted for different tasks on the fly. Available models and networks optimized for edge ML inference are presented in this study as well. Such models are more appropriate for space applications since a custom implementation is time-consuming and prone to failure due to tight FPGA fabric timing constraints. Nevertheless, the workflow for a custom Artificial Neural Network (ANN) implementation on an FPGA has been presented. Xilinx Kintex-7 FPGA has been used as the target FPGA in the implementation and evaluation of the network. Vivado
机译：机器学习（ML）技术越来越多地用于陆地应用。他们也已提出各种空间应用，例如车载数据处理，行星探索，自主操作和各种特定于任务的应用程序。通过云服务，强大的台式计算机和框架API等共享资源，促进了ML的陆地应用。但是，这些推动者在太空环境中受到限制。要利用ML的进展，空间应用需要包含在板上推论。本文的第一部分提出了关于一些空间应用的综述，其中这种算法适合或已被使用。审查范围仅限于已经实现的载机和飞行中的应用程序，而不是在提案阶段的大多数人中的因素。不涵盖用于分析和处理数据的地面站M1技术。审查还提出了一些已经或可以在这些实施中使用的硬件。虽然已经过度朝着ML推断到边缘的硬件平台上，但该评论说明了现场可编程门阵列（FPGA）和相关系统的片上（SOC）更适合于空间应用中的ML推理。它们还有飞行遗产，已应用于特定特定和卫星子系统操作。它们也可以重新配置，因此可以在飞行中为不同的任务采用。本研究还列出了针对Edge ML推理优化的可用型号和网络。这种模型更适合空间应用，因为自定义实施是耗时并且由于紧密的FPGA结构时序约束而容易发生故障。然而，已经介绍了FPGA上的定制人工神经网络（ANN）实现的工作流程。 Xilinx Kintex-7 FPGA已被用作网络实施和评估中的目标FPGA。 Vivado

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