Summary of Ripple: Accelerating Llm Inference on Smartphones with Correlation-aware Neuron Management, by Tuowei Wang et al.
Ripple: Accelerating LLM Inference on Smartphones with Correlation-Aware Neuron Management
by Tuowei Wang, Ruwen Fan, Minxing Huang, Zixu Hao, Kun Li, Ting Cao, Youyou Lu, Yaoxue Zhang, Ju Ren
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Operating Systems (cs.OS); Performance (cs.PF)
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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 addresses the challenge of deploying Large Language Models (LLMs) on mobile devices, which requires reducing their computational and memory demands without compromising model accuracy. While lightweight LLMs have been developed to fit mobile environments, they suffer from degraded model accuracy. In contrast, this paper explores sparsity-based techniques that minimize DRAM usage by selectively transferring only relevant neurons to DRAM while retaining the full model in external storage. However, such approaches are critically limited by numerous I/O operations, particularly on smartphones with severe IOPS constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it possible to use super powerful language models on mobile phones, even though they need a lot of computing power and memory. Right now, there’s no good way to do this without losing some of the model’s ability to understand and generate text. The researchers are trying to find a better solution by only using the parts of the model that are really important for each task, but this is tricky because it needs to happen fast enough to work well on a phone. |
