Summary of Efficient Noise Mitigation For Enhancing Inference Accuracy in Dnns on Mixed-signal Accelerators, by Seyedarmin Azizi et al.
Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on Mixed-Signal Accelerators
by Seyedarmin Azizi, Mohammad Erfan Sadeghi, Mehdi Kamal, Massoud Pedram
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 a framework to improve the robustness of neural models in analog computing systems. By introducing a denoising block between selected layers of a pre-trained model, it demonstrates significant increases in robustness against various levels of noise. To minimize overhead, an exploration algorithm identifies optimal insertion points for the denoising blocks. A specialized architecture is also proposed to efficiently execute these blocks within mixed-signal accelerators. Evaluations using DNN models trained on ImageNet and CIFAR-10 datasets show that the approach can reduce accuracy drops due to variations by 90.84%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes analog neural networks better by stopping noise from making mistakes. It adds a special block between some layers of the network that helps clean up noisy signals. The block is smart enough to find where it should be added so it doesn’t waste too much extra processing power. This can make computers with both digital and analog parts work faster and more accurately. |
