Summary of David and Goliath: An Empirical Evaluation Of Attacks and Defenses For Qnns at the Deep Edge, by Miguel Costa and Sandro Pinto
David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge
by Miguel Costa, Sandro Pinto
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 A novel approach to deploying artificial neural networks (ANNs) on resource-constrained microcontrollers (MCUs), such as Arm Cortex-M, has been proposed. By leveraging edge computing and quantization techniques, researchers aim to enable intelligence at the deep edge, reducing data exposure and ensuring reliable real-time applications. The study focuses on understanding the robustness of quantized neural networks (QNNs) against adversarial examples, a crucial challenge in deploying ANNs on constrained devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being developed to put artificial brain power into tiny computers called microcontrollers (MCUs). These little computers are really cheap and don’t use much energy. By using this new approach, scientists hope to make it possible for these small computers to learn and think like humans. The research looks at how well these special kinds of computer networks do when they’re attacked with bad data. |
Keywords
» Artificial intelligence » Quantization
