3 papers
cs.LG2026
QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations
Zhixiong Zhao, Haomin Li, Fangxin Liu +5
Transformer-based models have revolutionized computer vision (CV) and natural language processing (NLP) by achieving state-of-the-art performance across a range of benchmarks. Howe…
cs.LG2025
LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation
Fangxin Liu, Ning Yang, Junping Zhao +3
Large language models (LLMs) have achieved significant progress in natural language processing but face challenges in deployment due to high memory and computational requirements.…
cs.CL2025
DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies
Ning Yang, Fangxin Liu, Junjie Wang +4
Large language models (LLMs) have achieved remarkable performance across a wide range of NLP tasks. However, their substantial inference cost poses a major barrier to real-world de…