Summary of Post-training Statistical Calibration For Higher Activation Sparsity, by Vui Seng Chua et al.
Post-Training Statistical Calibration for Higher Activation Sparsity
by Vui Seng Chua, Yujie Pan, Nilesh Jain
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Statistical Calibrated Activation Pruning (SCAP), a novel framework for post-training activation pruning in Fully-Connected layers that can be applied to various models, including Transformers. SCAP combines two key components: mode-centering technique for pre-calibrating activation distributions and statistical calibration for generalizing sparsification by input activations. The results show that SCAP achieves robust Pareto efficiency compared to prior methods, leading to a 1.5x speedup in decoding time while maintaining model quality. SCAP is demonstrated to be effective across various models, including Transformer Decoders, MoE, Mamba2, Encoding Transformer, and pre-quantized models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SCAP is a new way to make computer models faster without losing their ability to understand language. It works by removing some of the connections between the different parts of the model, which can be done after the model has been trained on lots of data. This makes it more efficient and allows it to process information faster. |
Keywords
* Artificial intelligence * Pruning * Transformer
