4 papers
HiFloat4 Format for Language Model Pre-training on Ascend NPUs
Mehran Taghian, Yunke Peng, Xing Huang +22
Large foundation models have become central to modern machine learning, with performance scaling predictably with model size and data. However, training and deploying such models i…
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats
Pengxiang Zhao, Hui-Ling Zhen, Xing Li +10
As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiFloat (HiF8 and HiF4), a family…
HiFloat4 Format for Language Model Inference
Yuanyong Luo, Jing Huang, Yu Cheng +19
This paper introduces HiFloat4 (HiF4), a block floating-point data format tailored for deep learning. Each HiF4 unit packs 64 4-bit elements with 32 bits of shared scaling metadata…
BAPS: A Fine-Grained Low-Precision Scheme for Softmax in Attention via Block-Aware Precision reScaling
Zisheng Ye, Xiaoyu He, Maoyuan Song +10
As the performance gains from accelerating quantized matrix multiplication plateau, the softmax operation becomes the critical bottleneck in Transformer inference. This bottleneck…