Hybrid Convolutional Neural Networks with Reliability Guarantee
This work addresses the need for dependable AI execution in safety-critical applications, though it appears incremental as it builds on existing redundancy techniques.
The paper tackles the problem of ensuring reliable execution of AI models by proposing a hybrid CNN that integrates reliable and non-reliable execution to reduce computational expense, with preliminary results showing potential efficiency gains.
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.