Summary of Coreinfer: Accelerating Large Language Model Inference with Semantics-inspired Adaptive Sparse Activation, by Qinsi Wang et al.
CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
by Qinsi Wang, Saeed Vahidian, Hancheng Ye, Jianyang Gu, Jianyi Zhang, Yiran Chen
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 introduces CoreInfer, a novel method for accelerating inference in large language models (LLMs) using adaptive sparse activation. This approach reduces computational costs and memory demands without degrading performance, making it suitable for resource-constrained hardware devices. CoreInfer is based on sentence-level prediction of core neurons, which are the subset of critical neurons that contribute most to a given sentence’s semantics. The method outperforms existing approaches in terms of model generalization and task generalization across various models and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoreInfer is a new way to make large language models run faster without sacrificing their power. Right now, these models use too much computer power and memory when they’re used for tasks like text understanding or generation. This makes it hard to use them on devices that have limited resources. The CoreInfer method finds the most important “core” neurons in a sentence that make it meaningful. It then uses this information to predict which neurons will be needed next, so it can skip over unimportant ones and save time and energy. |
Keywords
» Artificial intelligence » Generalization » Inference » Semantics
