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cs.AR2026
MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration
Yuan Liao, Jae-sun Seo
The deployment of Vision-Language Models (VLMs) on edge devices is severely bottlenecked by memory bandwidth, necessitating aggressive sub-8-bit quantization. Since edge accelerato…
cs.LG2026
HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference
Chun-Ting Chen, Dongmin Han, Hangyeol Mun +6
Block Quantization (BQ) enables efficient LLM inference by quantizing both weights and activations, but its design space remains underexplored. Through hardware-accuracy design spa…