通过在电信设备平台上进行时间上采样和可训练编码实现光学神经形态计算
Optical neuromorphic computing via temporal up-sampling and trainable encoding on a telecom device platform
AI总结：
提出利用商用电信设备通过时间上采样和可训练编码掩码将光信号非线性映射到高维空间，在RC和ELM模型中实现了灵活的光学神经形态计算。
摘要：
将输入信号映射到高维空间是各种神经形态计算范式（包括 Reservoir Computing (RC) 和 Extreme Learning Machines (ELM) 等模型）中的关键概念。我们提出使用商用电信设备和为高速光数据传输开发的技术，通过将光信号非线性映射到可应用线性处理的高维空间来实现这些模型。我们通过对输入信号应用时间上采样（以商用电信设备的速度）和成熟的波分复用（WDM）来控制输出特征维度。我们的上采样方法利用可训练的编码掩码，其中每个输入符号被替换为结构化的掩码符号序列，有效地增加了特征空间的表示能力。这为输入信号的动态相位掩蔽提供了显著的灵活性。我们在 RC 和 ELM 的背景下演示了这种方法，使用了现成的光子器件，包括半导体光放大器和非线性 Mach-Zehnder 干涉仪（MZI）。我们研究了这些器件提供的非线性映射如何根据输出状态的可控可分离性和可预测性的增加来表征。
英文摘要：
Mapping input signals to a high-dimensional space is a critical concept in various neuromorphic computing paradigms, including models such as Reservoir Computing (RC) and Extreme Learning Machines (ELM). We propose using commercially available telecom devices and technologies developed for high-speed optical data transmission to implement these models through nonlinear mapping of optical signals into a high-dimensional space where linear processing can be applied. We manipulate the output feature dimension by applying temporal up-sampling (at the speed of commercially available telecom devices) of input signals and a well-established wave-division-multiplexing (WDM). Our up-sampling approach utilizes a trainable encoding mask, where each input symbol is replaced with a structured sequence of masked symbols, effectively increasing the representational capacity of the feature space. This gives remarkable flexibility in the dynamical phase masking of the input signal. We demonstrate this approach in the context of RC and ELM, employing readily available photonic devices, including a semiconductor optical amplifier and nonlinear Mach-Zender interferometer (MZI). We investigate how nonlinear mapping provided by these devices can be characterized in terms of the increased controlled separability and predictability of the output state.
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