Summary of Efqat: An Efficient Framework For Quantization-aware Training, by Saleh Ashkboos et al.
EfQAT: An Efficient Framework for Quantization-Aware Training
by Saleh Ashkboos, Bram Verhoef, Torsten Hoefler, Evangelos Eleftheriou, Martino Dazzi
First submitted to arxiv on: 17 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 A new scheme for quantization-aware training (QAT) has been proposed to achieve near-full precision accuracy in a computationally efficient manner. The method, called EfQAT, generalizes both QAT and post-training quantization (PTQ) schemes by optimizing only a subset of the parameters of a quantized model. EfQAT starts with PTQ and then updates the most critical network parameters while freezing the rest, accelerating the backward pass. This approach is demonstrated to be more accurate than PTQ with little extra compute, and it can accelerate the QAT backward pass between 1.44-1.64x while retaining most accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EfQAT is a new way to make computer models work faster without losing much accuracy. It’s like a shortcut that only updates some parts of the model instead of all of them, which makes it more efficient. This method works well on different types of models and can even speed up how fast they work by 1-2 times while still being pretty accurate. |
Keywords
» Artificial intelligence » Precision » Quantization
