Summary of Activation Sparsity Opportunities For Compressing General Large Language Models, by Nobel Dhar et al.
Activation Sparsity Opportunities for Compressing General Large Language Models
by Nobel Dhar, Bobin Deng, Md Romyull Islam, Kazi Fahim Ahmad Nasif, Liang Zhao, Kun Suo
First submitted to arxiv on: 13 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 The paper explores the deployment of Large Language Models (LLMs) on edge devices to enhance their independent capabilities. This is achieved by investigating activation sparsity, a technique that can compress AI models while maintaining accuracy. The study focuses on Feed-Forward Network (FFN) components, which typically account for two-thirds of parameters in LLMs. Empirical analysis shows that around 50% of main memory and computing reductions can be obtained with negligible accuracy degradation. This extra sparsity does not exist naturally in current LLMs, requiring tuning activation outputs by injecting zero-enforcing thresholds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make Large Language Models work better on devices like smartwatches or smartphones. It finds a way to shrink the size of these models without losing their ability to understand and generate text. This is important because it could help devices do more tasks on their own, without needing to send requests back and forth with bigger computers. |
