通过冗余实现依赖神经网络：冗余结构的比较
Dependable Neural Networks Through Redundancy, A Comparison of Redundant Architectures
摘要：
随着边缘人工智能在现实世界中的应用越来越多，特别是在工业中，使用人工智能的功能安全应用程序的问题已经开始被提出。在这方面的工作，我们探讨的问题，实现可靠的操作的神经网络。我们讨论的可靠性问题，在一般的实施条款之前，检查锁步解决方案。我们的直觉是，两个相似的神经网络不一定在同一时间产生结果，并且平台之间需要同步。我们进行了一些初步的测量，可以支持这种直觉，并介绍了一些工作，在实现锁步神经网络引擎。
英文摘要：
With edge-AI finding an increasing number of real-world applications, especially in industry, the question of functionally safe applications using AI has begun to be asked. In this body of work, we explore the issue of achieving dependable operation of neural networks. We discuss the issue of dependability in general implementation terms before examining lockstep solutions. We intuit that it is not necessarily a given that two similar neural networks generate results at precisely the same time and that synchronization between the platforms will be required. We perform some preliminary measurements that may support this intuition and introduce some work in implementing lockstep neural network engines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
