Summary of Fast On-device Llm Inference with Npus, by Daliang Xu et al.
Fast On-device LLM Inference with NPUs
by Daliang Xu, Hao Zhang, Liming Yang, Ruiqi Liu, Gang Huang, Mengwei Xu, Xuanzhe Liu
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 focuses on improving on-device inference for Large Language Models (LLMs), particularly mobile-sized models like Gemma-2B. The authors highlight the significant challenge of high inference latency, which is often bottlenecked by the prefill stage in tasks such as screen UI understanding. To address this issue, they propose a novel approach to reduce inference latency without sacrificing accuracy. The paper presents an efficient prefill strategy and evaluates its performance on various benchmarks, including UI understanding tasks. The authors utilize Gemma-2B, a mobile-sized LLM, as the primary model for their experiments. They demonstrate that their proposed approach can significantly reduce inference latency while maintaining accurate results. This research contributes to the development of more practical and efficient on-device inference methods for large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand what’s happening on your phone screen just by looking at it! That’s basically what this paper is about – making computers understand text and images really fast, even when they’re not connected to the internet. Right now, these computers are too slow because they need to “warm up” before doing tasks like recognizing things on a screen. The researchers found that this warming-up process takes way too long, so they came up with a clever trick to make it faster without sacrificing accuracy. They used a special kind of computer model called Gemma-2B and tested their idea on some cool applications. The results are really promising – the computers can now understand things on your screen quickly and accurately! |
Keywords
» Artificial intelligence » Inference
