Summary of Revisiting Dnn Training For Intermittently-powered Energy-harvesting Micro-computers, by Cyan Subhra Mishra et al.
Revisiting DNN Training for Intermittently-Powered Energy-Harvesting Micro-Computers
by Cyan Subhra Mishra, Deeksha Chaudhary, Jack Sampson, Mahmut Taylan Knademir, Chita Das
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 study introduces a novel training methodology for Deep Neural Networks (DNNs) operating in energy-constrained environments like Energy Harvesting Wireless Sensor Networks. The primary challenge is the intermittent power availability. To address this, the authors propose a dynamic dropout technique that adapts to device architecture and energy harvesting variability. This method optimizes dropout rates during training by incorporating device-specific parameters and energy profiles. By modulating the training process based on predicted energy availability, the approach conserves energy while ensuring sustained learning and inference capabilities under power constraints. The proposed strategy achieves 6-22% accuracy improvements compared to the state of the art with less than 5% additional compute. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for computers to learn in situations where they don’t always have enough power. Right now, devices like wireless sensors can run out of battery at any time. To solve this problem, researchers created a special training method for deep learning networks that works with energy harvesting technology. This means the network learns how to adapt to changing energy levels and make adjustments on its own. The result is more accurate predictions and better performance even when power is limited. |
Keywords
* Artificial intelligence * Deep learning * Dropout * Inference
