一个全的基于memristor的内存计算系统,具有软硬件共同开发的系统
1School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
Nature communications
|March 3, 2025
概括
本研究介绍了基于memristor的内存计算 (CIM) 系统的软硬件联合开发方法. 这种方法提高了灵活性和效率,提高了神经网络在训练和推理过程中的准确性.
科学领域:
- 计算机工程 计算机工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 基于memristor的内存计算 (CIM) 系统由于硬件设计约束和手动参数调整而面临实用性的限制.
- 优化CIM系统对于高效和强大的人工智能硬件至关重要.
研究的目的:
- 开发一种软硬件共同开发方法,以提高基于memristor的CIM系统的灵活性和效率.
- 提高神经网络模型对硬件非理想性和模拟计算噪声的稳定性.
主要方法:
- 实现了一个灵活的硬件组件,支持各种数据流和映射策略.
- 开发了用于自动模型放置和高效优化技术的软件.
- 集成的软件和硬件,提供完整的共同开发解决方案.
主要成果:
- 在四个任务中展示了六个神经网络模型中的系统.
- 在培训期间,ResNet-32的准确性提高了4.76%.
- 在芯片上的推断过程中,在模型中观察到3.32%至9.45%的准确性改进.
结论:
- 拟议的软硬件联合开发方法显著提高了基于memristor的CIM系统的性能和实用性.
- 自动优化方法提高模型的稳定性和抑制噪声,从而提高AI应用的准确性.
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