3 citations · 8 across the 12 of their papers we have counts for
4 papers · 1 filter
Improving Quantization-aware Training of Low-Precision Network via Block Replacement on Full-Precision Counterpart
Chengting Yu, Shu Yang, Fengzhao Zhang +3
Quantization-aware training (QAT) is a common paradigm for network quantization, in which the training phase incorporates the simulation of the low-precision computation to optimiz…
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference
Chao Zeng, Songwei Liu, Shu Yang +3
Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper presents Group Quantization and Sparse Acceleration (GQSA), a n…
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Chao Zeng, Songwei Liu, Yusheng Xie +6
Large Language Models (LLMs) have revolutionized natural language processing tasks. However, their practical application is constrained by substantial memory and computational dema…
SDiT: Spiking Diffusion Model with Transformer
Shu Yang, Hanzhi Ma, Chengting Yu +2
Spiking neural networks (SNNs) have low power consumption and bio-interpretable characteristics, and are considered to have tremendous potential for energy-efficient computing. How…