AASD: Accelerate Inference by Aligning Speculative Decoding in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) have achieved notable success in visual instruction tuning, yet their inference is time-consuming due to the auto-regressive decoding of Large Language Model (LLM) backbone. Traditional methods for accelerating inference, including model compression and migra...

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Bibliographic Details

Published in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors:	Yang, Chaoqun, Chen, Ran, Zhang, Muyang, Pang, Weiguang, Chen, Yuzhi, Xu, Rongtao, Fu, Kexue, Wang, Changwei, Gao, Longxiang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22.06.2025
Subjects:	Computational modeling Decoding Faces Feature extraction Focusing Inference Acceleration Large language models Model compression Multimodal Large Language Model Question answering (information retrieval) Speculative Decoding Tuning Visualization
Online Access:	Get full text
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