通过复杂值网络定量化的超高效物理场计算
Zihan Geng1, Zhilin Li2, Mi Zhou2
1Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China. geng.zihan@sz.tsinghua.edu.cn.
Nature communications
|March 11, 2026
概括
我们为复杂值的神经网络开发了一种新的真假联合量化方法. 这种技术在相位敏感应用中显著提高了性能,同时大幅减少了计算负载和内存使用.
科学领域:
- 人工智能的人工智能
- 信号处理 信号处理
- 计算物理 计算物理
背景情况:
- 神经网络定量化包括实值模型,但对于复杂值网络来说是不发达的.
- 传统方法破坏复杂乘法的代数结构,扭曲相位关系.
研究的目的:
- 开发一种量子化方法,在复杂值的神经网络中保持振幅相忠度.
- 为科学计算提供高效,高性能的复杂值网络.
主要方法:
- 为复杂的乘法提出了一个真假的联合量化方法.
- 综合物理意识的自适应精度培训.
- 对全息图生成,音频,无线和SAR信号识别任务的评估性能.
主要成果:
- 与HoloNet相比,在全息图生成的峰值信号噪声比率 (3.9dB) 中取得了显著的改进.
- 减少了99.1%的计算负载和99.8%的内存消耗.
- 在各种阶段敏感任务中表现出色的表现.
结论:
- 现实-想象的联合量子化方法有效地保持了振幅相忠度.
- 这种方法可以实现轻量级,高保真度的复杂值神经网络.
- 为先进的科学计算和连贯的信号处理铺平了道路.
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