12 citations · 12 across the 3 of their papers we have counts for
4 papers · 1 filter
FluxBin: Flexible LUT-based Ultra-low-bit LLM Inference by Algorithm-Kernel Synergy
Qingyao Yang, Runming Yang, He Xiao +7
While binary quantization theoretically promises extreme compression and acceleration for Large Language Models (LLMs), existing research often overlooks the necessity of specializ…
BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models
Junyu Chen, Jungang Li, Jing Xiong +11
Large language model inference is often bounded by memory footprint and bandwidth in resource-constrained deployments, making quantization fundamental to efficient serving. While p…
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuning
Junyu Chen, Junzhuo Li, Zhen Peng +4
Quantization and fine-tuning are crucial for deploying large language models (LLMs) on resource-constrained edge devices. However, fine-tuning quantized models presents significant…
RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation
Junyu Chen, Long Shi, Badong Chen
Graph Neural Networks (GNNs) have exhibited remarkable efficacy in learning from multi-view graph data. In the framework of multi-view graph neural networks, a critical challenge l…