Summary of Hybrid Convolutional Neural Networks with Reliability Guarantee, by Hans Dermot Doran and Suzana Veljanovska
Hybrid Convolutional Neural Networks with Reliability Guarantee
by Hans Dermot Doran, Suzana Veljanovska
First submitted to arxiv on: 8 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a method to ensure dependable AI models by using redundant execution techniques. The authors integrate reliable model execution with non-reliable execution, minimizing the additional computational expense. They focus on a co-design approach, applying this technique to accelerate AI-accelerators without documented safety or dependability properties. This generic solution extends the application scope of existing AI-accelerators. A hybrid CNN is described, providing preliminary results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models need to be safe and dependable. To achieve this, researchers propose a way to ensure reliable execution using redundant techniques. This means running the model multiple times to ensure it works correctly. The extra computation needed for redundancy can be minimized by combining reliable and non-reliable executions. This approach is useful for AI-accelerators that don’t have built-in safety features. The authors demonstrate this method with a special type of neural network called a hybrid CNN. |
Keywords
» Artificial intelligence » Cnn » Neural network
