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带有电光模拟内存的神经形态光子计算
Sean Lam1, Ahmed Khaled2, Simon Bilodeau3
1Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada. seanlm@student.ubc.ca.
Nature communications
|February 7, 2026
概括
我们开发了一个与神经形态光子电路集成的模拟电子内存,以降低能源成本. 这项创新可以在机器学习任务中节省超过26倍的电力,从而实现高效,高速的计算.
科学领域:
- 神经形态工程的神经形态工程
- 这是光子计算.
- 机器学习硬件 机器学习硬件
背景情况:
- 神经形光子系统依赖于模拟信号,需要能源密集的数字对模拟转换器 (DAC) 和模拟对数字转换器 (ADC).
- 传统的·诺伊曼架构由于存储器和转换器之间的数据移动而面临重大能源成本.
研究的目的:
- 提出和演示一个与光子计算单元直接集成的模拟电子内存.
- 为了消除耗费能源的数据移动,减少对神经形态光子系统中DAC/ADC的依赖.
主要方法:
- 一个神经形态光子电路与芯片上的电容模拟内存的单立体集成.
- 使用机器学习进行现场培训和在MNIST数据集上推断的性能评估.
主要成果:
- 与传统的SRAM-DAC架构相比,实现了超过26倍的节能.
- 演示了>90%的推断准确度,最小模拟内存保留与网络延迟比率为100.
- 展示了漏洞模拟内存的可行性,而没有显著的性能降低.
结论:
- 将模拟内存集成到神经形态光子架构中,为节能,高速计算提供了一个可扩展的途径.
- 这种方法大大减少了数据的移动和对转换器的依赖.
- 为高级AI任务实现神经形态光子系统的实际实现.
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