Deep learning (DL) presents new opportunities for enabling spacecraft autonomy, onboard analysis, and intelligent applications for space missions. However, DL applications are computationally intensive and often infeasible to deploy on radiation-hardened (rad-hard) processors, which traditionally harness a fraction of the computational capability of their commercial-off-the-shelf counterparts. Commercial FPGAs and system-on-chips present numerous architectural advantages and provide the computation capabilities to enable onboard DL applications; however, these devices are highly susceptible to radiation-induced single-event effects (SEEs) that can degrade the dependability of DL applications. In this article, we propose Reconfigurable ConvNet (RECON), a reconfigurable acceleration framework for dependable, high-performance semantic segmentation for space applications. In RECON, we propose both selective and adaptive approaches to enable efficient SEE mitigation. In our selective approach, control-flow parts are selectively protected by triple-modular redundancy to minimize SEE-induced hangs, and in our adaptive approach, partial reconfiguration is used to adapt the mitigation of dataflow parts in response to a dynamic radiation environment. Combined, both approaches enable RECON to maximize system performability subject to mission availability constraints. We perform fault injection and neutron irradiation to observe the susceptibility of RECON and use dependability modeling to evaluate RECON in various orbital case studies to demonstrate a 1.5–3.0× performability improvement in both performance and energy efficiency compared to static approaches.

中文翻译：

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用于空间应用的弹性语义分割的可重构框架

深度学习 (DL) 为实现航天器自主性、机载分析和太空任务智能应用提供了新的机会。然而，深度学习应用程序的计算量很大，并且通常无法部署在抗辐射（rad-hard）处理器上，这些处理器传统上利用其商用现货对应物的一小部分计算能力。商用 FPGA 和片上系统具有众多架构优势，并提供计算能力以支持板载 DL 应用；然而，这些设备极易受到辐射诱发的单粒子效应 (SEE) 的影响，这会降低深度学习应用的可靠性。在本文中，我们提出了 Reconfigurable ConvNet (RECON)，一种可重构的可靠加速框架，用于空间应用的高性能语义分割。在 RECON 中，我们提出了选择性和自适应方法来实现有效的 SEE 缓解。在我们的选择性方法中，控制流部分受到三重模块冗余的选择性保护，以最大限度地减少 SEE 引起的挂起，在我们的自适应方法中，部分重构用于调整数据流部分的缓解以响应动态辐射环境。结合起来，这两种方法使 RECON 能够在任务可用性限制的情况下最大限度地提高系统性能。我们进行故障注入和中子辐照以观察 RECON 的敏感性，并使用可靠性模型在各种轨道案例研究中评估 RECON，以证明与静态方法相比，性能和能源效率的性能提高了 1.5-3.0 倍。