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CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models
- Vision-Language Models (VLMs) face high inference costs in time and memory.
- Token sparsity and neuron sparsity offer solutions to improve efficiency in VLMs.
- A new study explores the interplay between Core Neurons and Core Tokens in VLMs.
- The study introduces CoreMatching, a framework leveraging token and neuron sparsity for enhanced inference efficiency, achieving significant speedup.
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