概括
我们开发了一种新的方法来减少神经网络均衡器中的计算复杂性,以实现更快,实时的光通信系统. 这种方法可以显著减少内存的使用,而不会造成性能损失.
科学领域:
- 光学通信是指光学通信.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 基于神经网络 (NN) 的等分器擅长缓解强度调制直接检测 (IM/DD) 系统中的非线性损伤.
- NNs的高计算复杂性 (CC) 阻碍了它们在光学接收器设计中的实时应用.
研究的目的:
- 提出一种新的重量适应,混合精度量化和修剪方法,以减少基于NN的等分器的CC.
- 为了使实时处理只使用整数算术,从而减少硬件资源消耗.
主要方法:
- 引入了一个重量适应的联合混合精度量化和修剪策略.
- NN连接被修剪或定量到特定的比特宽度,创建一个混合压缩稀疏网络.
- 该方法仅使用整数算术,避免浮点运算.
主要成果:
- 该方法在使用直接调制激光 (DML) 的50Gb/s,25公里的PAM-4 IM/DD链路上得到了验证.
- 与标准NNs相比,在最小网络大小下实现了大约80%的内存节省,而没有降低性能.
- 量子化对于过度参数化的NN来说比最小尺寸的NN更有效.
结论:
- 提出的方法有效地降低了基于NN的等分器的计算复杂性和硬件资源需求.
- 这种技术有助于在光通信系统中实时实现先进的NN等分器.
- 混合精度量化和修剪为优化资源有限的环境中的NN性能提供了可行的解决方案.
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