概括
我们使用内核修剪开发了一个基于的光子卷积神经网络 (PCNN). 这种方法显著降低了能源消耗,同时保持了光学计算应用的高精度.
科学领域:
- 光子学 是一个光子学.
- 人工智能的人工智能
- 计算机工程 计算机工程
背景情况:
- 基于的光学神经网络承诺在集成光子电路上实现高性能计算.
- 芯片上光学深度网络的可扩展性受到能源和空间限制的限制.
研究的目的:
- 介绍一个基于的光子卷积神经网络 (PCNN),与内核修剪集成.
- 为了解决当前光学神经网络的可扩展性限制.
主要方法:
- 开发了一个PCNN,具有可调节的微环共振器重量库作为光学卷积计算核心.
- 通过数值模拟研究了权重映射精度对PCNN性能的影响.
- 在MNIST数据集上实验证明了PCNN的准确性,并进行了显著的内核修剪.
主要成果:
- 由于重量映射精度低于4.3位,PCNN性能显著下降,重量映射精度低于4.3位.
- 实验结果显示,MNIST的精度损失最小,即使在削减了93.75%的卷积内核之后.
- 核子修剪节省了大约202.3mW的核子,节省的能量与修剪的核子数量线性成比例.
结论:
- 核削减是提高基于的PCNN的能源效率的可行策略.
- 拟议的方法可扩展,为光子集成电路上的更快,更节能的大规模光学卷积神经网络提供了一条道路.
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