状态空间模型的内存计算实现,用于事件序列处理
Xiaoyu Zhang1, Mingtao Hu1, Sen Lu1
1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
国家空间模型 (SSM) 现在在新硬件上是高效的. 这项研究将SSM与内存计算硬件集成,用于人工智能任务中的实时事件驱动处理.
科学领域:
- 人工智能的人工智能
- 计算机工程 计算机工程
- 神经科学是一个神经科学.
背景情况:
- 国家空间模型 (SSM) 提供先进的长序处理能力.
- SSM 将循环和卷积网络泛化,模仿生物系统的功能.
- 现有的SSM实施面临能源效率和实时处理方面的挑战.
研究的目的:
- 在节能计算内存硬件上实现状态空间模型 (SSM).
- 为实现人工智能应用程序的实时,事件驱动的处理.
- 探索算法和硬件共同设计以提高性能.
主要方法:
- 对实值系数和共享衰变常数进行重新参数化的SSM.
- 杆设备动态和对角化状态过渡参数.
- 在基于交叉的内存计算系统中与memristors本地实现了状态演变.
主要成果:
- 使用拟议的系统,在AI任务中实现了高精度.
- 与传统方法相比,证明了显著的能源效率.
- 启用完全异步处理以实现基于事件的视觉和音频.
结论:
- 算法和硬件的共同设计使SSM的高效实施成为可能.
- 该系统为实时,低功耗的人工智能处理提供了一条途径.
- 这种方法适用于基于事件的感觉数据任务.
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