  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.
