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可靠需要生成可靠的模型，并确保这些模型被可靠执行。我们提出将冗余执行作为一种成熟的技术手段，用于保障AI模型的可靠运行。该通用技术能够扩展那些缺乏完善安全性或可靠性文档的AI加速器的应用范围。传统冗余技术通常需要付出至少两倍或三倍于原始计算的开销。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，仅在绝对必要的环节投入额外计算资源。本文描述了混合型CNN的设计方案、实现过程及初步实验结果。