Hybrid Convolutional Neural Networks with Reliability Guarantee

Abstract

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.

2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2024). Dependable and Secure Machine Learning Workshop (DSML 2024), Brisbane, Australia, June 24-27, 2024