This preliminary white paper proposes a novel 8-bit floating-point data format HiFloat8 (abbreviated as HiF8) for deep learning. HiF8 features tapered precision. For normal value encoding, it provides 7 exponent values with 3-bit mantissa, 8 exponent values with 2-bit mantissa, and 16 exponent values with 1-bit mantissa. For denormal value encoding, it extends the dynamic range by 7 extra powers of 2, from 31 to 38 binades (notice that FP16 covers 40 binades). Meanwhile, HiF8 encodes all the special values except that positive zero and negative zero are represented by only one bit-pattern. Thanks to the better balance between precision and dynamic range, HiF8 can be simultaneously used in both forward and backward passes of AI training. In this paper, we will describe the definition and rounding methods of HiF8, as well as the tentative training and inference solutions. To demonstrate the efficacy of HiF8, massive simulation results on various neural networks, including traditional neural networks and large language models (LLMs), will also be presented.
@article{arxiv.2409.16626,
title = {Ascend HiFloat8 Format for Deep Learning},
author = {Yuanyong Luo and Zhongxing Zhang and Richard Wu and Hu Liu and Ying Jin and Kai Zheng and Minmin Wang and Zhanying He and Guipeng Hu and Luyao Chen and Tianchi Hu and Junsong Wang and Minqi Chen and Mikhaylov Dmitry and Korviakov Vladimir and Bobrin Maxim and Yuhao Hu and Guanfu Chen and Zeyi Huang},
journal= {arXiv preprint arXiv:2409.16626},
year = {2024}
}