Summary of Shrinking the Giant : Quasi-weightless Transformers For Low Energy Inference, by Shashank Nag et al.
Shrinking the Giant : Quasi-Weightless Transformers for Low Energy Inference
by Shashank Nag, Alan T. L. Bacellar, Zachary Susskind, Anshul Jha, Logan Liberty, Aishwarya Sivakumar, Eugene B. John, Krishnan Kailas, Priscila M. V. Lima, Neeraja J. Yadwadkar, Felipe M. G. Franca, Lizy K. John
First submitted to arxiv on: 4 Nov 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 paper presents Quasi Weightless Transformers (QuWeiT), a novel approach to build fast and energy-efficient transformer-based models, essential for widespread adoption in applications like chatbots, educational assistants, and visual recognition. By extending the Extended Finite Difference method for learning Look Up Table (LUT) networks, the authors integrate LUT-based weightless neural network layers with traditional transformers, significantly reducing computational complexity and energy consumption. QuWeiT achieves comparable accuracy to baseline transformer models on CIFAR-10, replacing 55% of multiplications while achieving a 2.2x energy efficiency gain. The proposed method also shows similar savings on the nanoGPT framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making transformers, which are like super smart computers, more efficient and use less energy. Transformers are used in many cool applications like chatbots, image recognition, and educational assistants. Right now, they take up too much energy and computing power, so the authors came up with a new way to make them work faster and use less energy. They call it Quasi Weightless Transformers (QuWeiT). It’s like a shortcut that makes the computer do its job more quickly and efficiently. In tests, this new method works just as well as the old one on some datasets, but uses much less energy. |
Keywords
» Artificial intelligence » Neural network » Transformer
