1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs
Chang Gao, Kang Zhao, Runqi Wang +2
Large language models (LLMs) have demonstrated impressive capabilities, but their enormous size poses significant challenges for deployment in real-world applications. To address t…
1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit
Chang Gao, Jianfei Chen, Kang Zhao +2
Fully quantized training (FQT) accelerates the training of deep neural networks by quantizing the activations, weights, and gradients into lower precision. To explore the ultimate…
Accelerating Transformer Pre-training with 2:4 Sparsity
Yuezhou Hu, Kang Zhao, Weiyu Huang +2
Training large transformers is slow, but recent innovations on GPU architecture give us an advantage. NVIDIA Ampere GPUs can execute a fine-grained 2:4 sparse matrix multiplication…
Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization
Haocheng Xi, Yuxiang Chen, Kang Zhao +3
Pretraining transformers are generally time-consuming. Fully quantized training (FQT) is a promising approach to speed up pretraining. However, most FQT methods adopt a quantize-co…