IM/DD光通信中神经形态硬件的脉冲神经网络非线性映射
Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communication
摘要：
实现尖峰神经网络（SNN）的神经形态计算是一项很有前途的技术，可以减少光收发器的占用空间，以满足数据中心流量快节奏增长的需要。在这项工作中，SNN非线性解映射器的设计和评估的模拟强度调制直接检测链路的色散。SNN解映射器在软件和模拟神经形态硬件系统BrainScaleS-2（BSS-2）上实现。为了比较，线性均衡（LE），Volterra非线性均衡（VNLE），和非线性解映射的人工神经网络（ANN）在软件中实现的考虑。在前向纠错误比特率为2 e-3时，软件SNN比LE好1.5 dB，比VNLE好0.3 dB，比ANN好0.5 dB。BSS-2上SNN的硬件代价仅为0.2 dB，即，而且，在硬件上，SNN的性能优于参考方法的所有软件实现。因此，这项工作表明，SNN解映射器实现的电气模拟硬件可以实现强大的和准确的信号处理满足严格的要求，光通信。
英文摘要：
Neuromorphic computing implementing spiking neural networks (SNN) is a promising technology for reducing the footprint of optical transceivers, as required by the fast-paced growth of data center traffic. In this work, an SNN nonlinear demapper is designed and evaluated on a simulated intensity-modulation direct-detection link with chromatic dispersion. The SNN demapper is implemented in software and on the analog neuromorphic hardware system BrainScaleS-2 (BSS-2). For comparison, linear equalization (LE), Volterra nonlinear equalization (VNLE), and nonlinear demapping by an artificial neural network (ANN) implemented in software are considered. At a pre-forward error correction bit error rate of 2e-3, the software SNN outperforms LE by 1.5 dB, VNLE by 0.3 dB and the ANN by 0.5 dB. The hardware penalty of the SNN on BSS-2 is only 0.2 dB, i.e., also on hardware, the SNN performs better than all software implementations of the reference approaches. Hence, this work demonstrates that SNN demappers implemented on electrical analog hardware can realize powerful and accurate signal processing fulfilling the strict requirements of optical communications.
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