Summary of Implications Of Noise in Resistive Memory on Deep Neural Networks For Image Classification, by Yannick Emonds et al.
Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification
by Yannick Emonds, Kai Xi, Holger Fröning
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Emerging Technologies (cs.ET); Performance (cs.PF)
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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 explores the limitations of using Resistive Memory as an alternative to SRAM. While it’s a promising technology, it requires careful control to ensure accurate read and write operations, which can be costly in terms of area, time, and energy. To mitigate these costs, the authors investigate how much noise is tolerable for image classification tasks based on neural networks. They introduce a noisy operator that simulates noise in a resistive memory unit and test its resilience using convolutional neural networks on the CIFAR-10 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make image recognition work better with noisy memories. Resistive Memory is an exciting new technology, but it’s not perfect – sometimes it makes mistakes when storing or retrieving information. To save time, energy, and space, scientists want to know how much noise they can tolerate while still getting good results. They created a special way of “noisy thinking” that mimics the errors in Resistive Memory and tested it on pictures using special computer programs. |
Keywords
* Artificial intelligence * Image classification
