In the last decade, deep learning techniques reached human-level performance in several specific tasks as image recognition, object detection, and adaptive control. For this reason, deep learning is being seriously considered by the industry to address difficult perceptual and control problems in several safety-critical applications (e.g., autonomous driving, robotics, and space missions). However, at the moment, deep learning software poses a number of issues related to safety, security, and predictability, which prevent its usage in safety-critical systems. This letter proposes a visionary software architecture that allows embracing deep learning while guaranteeing safety, security, and predictability by design. To achieve this goal, the architecture integrates multiple and diverse technologies, as hypervisors, run time monitoring, redundancy with diversity, predictive fault detection, fault recovery, and predictable resource management. Open challenges that stems from the proposed architecture are finally discussed.

中文翻译：

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用于安全关键系统深度学习的安全、可靠和可预测的软件架构

在过去十年中，深度学习技术在图像识别、对象检测和自适应控制等多项特定任务中达到了人类水平。出于这个原因，业界正在认真考虑深度学习来解决几个安全关键应用（例如，自动驾驶、机器人和太空任务）中的感知和控制困难问题。然而，目前，深度学习软件带来了许多与安全性、安保性和可预测性相关的问题，阻碍了其在安全关键系统中的使用。这封信提出了一个有远见的软件架构，允许拥抱深度学习，同时通过设计保证安全性、安全性和可预测性。为了实现这一目标，该架构集成了多种不同的技术，如管理程序、运行时监控、具有多样性的冗余、预测性故障检测、故障恢复和可预测的资源管理。最后讨论了源自提议架构的开放性挑战。