记忆式浮点里埃神经运算子网络,用于高效的科学建模
Jiancong Li1, Jing Tian1, Yudeng Lin2
1School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Huazhong University of Science and Technology, Wuhan 430074, China.
我们开发了一种新的计算在内存系统,以加速AI为科学模拟,特别是富里埃神经运算符 (FNO). 这种方法在复杂的科学建模任务中显著提高了能源效率.
科学领域:
- 科学的人工智能 (AI-for-Science) 是科学中的一种.
- 计算科学 计算科学
- 材料科学 材料科学 材料科学
背景情况:
- 像福里埃神经运算符 (FNO) 这样的AI-for-Science算法提供了高效的科学模拟能力.
- 传统的数字计算面临着FNO培训的广泛数据和高精度计算需求的挑战.
研究的目的:
- 展示异质计算在内存器 (CIM) 系统在加速人工智能为科学任务方面的潜力.
- 在CIM平台中利用精度有限的模拟设备进行高效的神经网络训练.
主要方法:
- 开发了一种异质的CIM系统,包括八个4千位的memristor芯片和嵌入式浮点计算.
- 实施了异质的培训计划,以加速浮点神经网络培训.
- 应用该系统来解决1D汉堡方程和3D导热模型.
主要成果:
- 实现了计算能效的显著提高,从116倍到21倍.
- 保持了与传统数字处理器相比的解决方案精度.
- 成功证明了该系统在解决复杂的科学建模问题方面的能力.
结论:
- 不同质的CIM系统有效地加速了AI-for-Science模拟,特别是基于FNO的任务.
- 这种方法将内存计算的适用性扩展到边缘AI之外.
- 这些发现有助于开发未来的AI-for-Science计算平台.
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