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首页> 外文期刊>Embedded Systems Letters, IEEE >A Safe, Secure, and Predictable Software Architecture for Deep Learning in Safety-Critical Systems
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A Safe, Secure, and Predictable Software Architecture for Deep Learning in Safety-Critical Systems

机译：安全，安全，可预测的软件架构，用于安全关键系统的深度学习

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摘要

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.
机译：在过去的十年中，深入学习技术在几个特定任务中达到了人为级别的性能作为图像识别，对象检测和自适应控制。出于这个原因，行业严重考虑了深度学习，以解决若干安全关键型应用中的困难感知和控制问题（例如，自主驾驶，机器人和太空任务）。然而，目前，深度学习软件造成了与安全，安全性和可预测性有关的许多问题，这防止了其在安全关键系统中的使用情况。这封信提出了一个有远见的软件架构，允许通过设计安全，安全性和可预测性来拥抱深度学习。为实现这一目标，该体系结构集成了多种多样的技术，作为虚拟机管理程序，运行时间监控，冗余与多样性，预测故障检测，故障恢复和可预测资源管理。终于讨论了拟议的架构源的开放挑战。

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