4 papers
SkipCat: Rank-Maximized Low-Rank Compression of Large Language Models via Shared Projection and Block Skipping
Yu-Chen Lu, Sheng-Feng Yu, Hui-Hsien Weng +5
Large language models (LLM) have achieved remarkable performance across a wide range of tasks. However, their substantial parameter sizes pose significant challenges for deployment…
UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs
Hung-Yueh Chiang, Chi-Chih Chang, Yu-Chen Lu +4
Deploying large language models (LLMs) on mobile platforms faces significant challenges due to the limited memory and shared computational resources of the device. Resource availab…
FLRC: Fine-grained Low-Rank Compressor for Efficient LLM Inference
Yu-Chen Lu, Chong-Yan Chen, Chi-Chih Chang +2
Although large language models (LLM) have achieved remarkable performance, their enormous parameter counts hinder deployment on resource-constrained hardware. Low-rank compression…
Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception
Chi-Chih Chang, Wei-Cheng Lin, Pei-Shuo Wang +4
In this work, we present an efficient and quantization-aware panoptic driving perception model (Q- YOLOP) for object detection, drivable area segmentation, and lane line segmentati…