Summary of Reducing Inference Energy Consumption Using Dual Complementary Cnns, by Michail Kinnas et al.
Reducing Inference Energy Consumption Using Dual Complementary CNNs
by Michail Kinnas, John Violos, Ioannis Kompatsiaris, Symeon Papadopoulos
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 research proposes a novel approach to reduce the energy requirements of Convolutional Neural Networks (CNNs) for inference tasks. The methodology employs two small Complementary CNNs that collaborate to produce higher confidence predictions, reducing energy consumption compared to using a single large deep CNN. Additionally, a memory component retains previous classifications for identical inputs, bypassing the need to re-invoke the CNNs and further saving energy. Experiments on a Jetson Nano computer demonstrate an energy reduction of up to 85.8% achieved on modified datasets with duplicated samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to make artificial intelligence (AI) work more efficiently on devices like phones or computers. Right now, AI models called Convolutional Neural Networks (CNNs) use a lot of power when they’re doing tasks like recognizing pictures. Researchers have been trying to find ways to make these models use less energy while still getting good results. The authors of this paper suggest using two small CNNs that work together and remember things they’ve seen before, which can save up to 85% of the energy needed. This is important because it could help devices run AI tasks without running out of battery too quickly. |
Keywords
» Artificial intelligence » Cnn » Inference
