Making AI safe and dependable requires the generation of dependable models and dependable execution of those models. We propose redundant execution as a well-known technique that can be used to ensure reliable execution of the AI model. This generic technique will extend the application scope of AI-accelerators that do not feature well-documented safety or dependability properties. Typical redundancy techniques incur at least double or triple the computational expense of the original. We adopt a co-design approach, integrating reliable model execution with non-reliable execution, focusing that additional computational expense only where it is strictly necessary. We describe the design, implementation and some preliminary results of a hybrid CNN.

翻译：实现人工智能的安全与可靠需要生成可靠的模型，并确保这些模型得到可靠执行。我们提出将冗余执行作为一种成熟技术，可用于保障AI模型的可靠运行。这种通用技术将拓展不具备完善文档化安全或可靠性属性的AI加速器的应用范围。典型的冗余技术至少会造成原计算成本的两倍或三倍开销。我们采用协同设计方法，将可靠模型执行与非可靠执行相结合，仅在确有必要的位置增加额外计算开销。本文阐述了混合CNN的设计方案、实现方式及部分初步结果。