# Hybrid Convolutional Neural Networks with Reliability Guarantee

arXiv: https://arxiv.org/abs/2405.05146 · HTML: https://mlsanity.com/paper/2405.05146/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 15 of 15)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Safe Model-based Reinforcement Learning with Stability Guarantees](https://mlsanity.com/paper/1705.08551/) (2017)
- [Unsolved Problems in ML Safety](https://mlsanity.com/paper/2109.13916/) (2021)
- [Safety Verification of Deep Neural Networks](https://mlsanity.com/paper/1610.06940/) (2016)
- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [Explainable Artificial Intelligence Approaches: A Survey](https://mlsanity.com/paper/2101.09429/) (2021)
- [HarDNN: Feature Map Vulnerability Evaluation in CNNs](https://mlsanity.com/paper/2002.09786/) (2020)
- [Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey](https://mlsanity.com/paper/2205.04712/) (2022)
- [Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures](https://mlsanity.com/paper/1610.07997/) (2016)
- [Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions](https://mlsanity.com/paper/2107.09546/) (2021)
- [Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision](https://mlsanity.com/paper/2108.07019/) (2021)
- [Dynamic Lockstep Processors for Applications with Functional Safety Relevance](https://mlsanity.com/paper/2107.08997/) (2021)
- [Overestimation learning with guarantees](https://mlsanity.com/paper/2101.11717/) (2021)

## Built on by (most cited first, 2 of 2)

- [An Early-Stage Workflow Proposal for the Generation of Safe and Dependable AI Classifiers](https://mlsanity.com/paper/2410.01850/) (2024)
- [Workflow for Safe-AI](https://mlsanity.com/paper/2503.14563/) (2025)

## Abstract (arXiv)

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.