Summary of Few-shot Testing: Estimating Uncertainty Of Memristive Deep Neural Networks Using One Bayesian Test Vector, by Soyed Tuhin Ahmed et al.
Few-Shot Testing: Estimating Uncertainty of Memristive Deep Neural Networks Using One Bayesian Test Vector
by Soyed Tuhin Ahmed, Mehdi Tahoori
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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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 proposed framework aims to improve confidence in neural network (NN) predictions made by hardware accelerators on memristor-based computation-in-memory (CIM) devices, which suffer from non-idealities such as defects and variations due to temperature, fabrication process, and runtime factors. To address this issue, the authors develop a Bayesian test vector generation framework that estimates model uncertainty for NNs implemented on CIM hardware. This approach is more generalizable across different model dimensions and requires only one test Bayesian vector storage in the hardware, achieving 100% coverage with 0.024 MB memory overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to make predictions using neural networks on special devices called memristor-based computation-in-memory (CIM) devices. These devices are used for edge computing and can speed up computations by performing operations inside the device’s memory. However, these devices have flaws that can affect the accuracy of the predictions. To fix this problem, the authors came up with a new way to generate test data that helps improve the confidence in the predictions made by these devices. |
Keywords
» Artificial intelligence » Neural network » Temperature
