Summary of Tiny Deep Ensemble: Uncertainty Estimation in Edge Ai Accelerators Via Ensembling Normalization Layers with Shared Weights, by Soyed Tuhin Ahmed et al.
Tiny Deep Ensemble: Uncertainty Estimation in Edge AI Accelerators via Ensembling Normalization Layers with Shared Weights
by Soyed Tuhin Ahmed, Michael Hefenbrock, Mehdi B. Tahoori
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This paper proposes the Tiny-Deep Ensemble approach, a low-cost method for uncertainty estimation on edge devices. The authors highlight the importance of functional safety in AI-driven systems, particularly in safety-critical domains like autonomous driving and medical diagnosis. Conventional methods for uncertainty estimation, such as deep ensembles and Monte Carlo dropout, are not suitable for battery-powered edge devices due to their high computation and memory requirements. In contrast, the Tiny-Deep Ensemble approach reduces storage requirements and latency by only normalizing layers M times, with shared weights and biases. This method also requires a single forward pass in hardware that allows batch processing. While it does not compromise accuracy, the Tiny-Deep Ensemble approach demonstrates improved inference accuracy (up to 1%) and reduced RMSE (17.17%) compared to state-of-the-art architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how AI can be used safely in important areas like self-driving cars and medical diagnosis. Right now, there are ways to measure how sure an AI system is about its predictions, but these methods use a lot of computing power and memory. The authors propose a new method that uses less computing power and memory, while still being accurate. This is important because some devices, like smartphones, don’t have enough power or memory to handle the old methods. The new approach does a better job than other AI systems at predicting things accurately. |
Keywords
» Artificial intelligence » Dropout » Inference
