VLM in a flash: I/O-Efficient Sparsification of Vision-Language Model via Neuron Chunking
Kichang Yang, Seonjun Kim, Minjae Kim, Nairan Zhang, chi zhang, Youngki Lee
Abstract
Edge deployment of large Vision-Language Models (VLMs) increasingly relies on flash-based weight offloading, where activation sparsification is used to reduce I/O overhead. However, conventional sparsification remains model-centric, selecting neurons solely by activation magnitude and neglecting how access patterns influence flash performance. We present Neuron Chunking, an I/O-efficient sparsification strategy that operates on *chunks*—groups of contiguous neurons in memory—and couples neuron importance with storage access cost. The method models I/O latency through a lightweight abstraction of access contiguity and selects chunks with high utility, defined as neuron importance normalized by estimated latency. By aligning sparsification decisions with the underlying storage behavior, Neuron Chunking improves I/O efficiency by up to 4.65× and 5.76× on Jetson Orin Nano and Jetson AGX Orin, respectively.
BibTeX
@inproceedings{
yang2025vlm,
title={{VLM} in a flash: I/O-Efficient Sparsification of Vision-Language Model via Neuron Chunking},
author={Kichang Yang and Seonjun Kim and Minjae Kim and Nairan Zhang and chi zhang and Youngki Lee},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=3xwsD68F2K}
}