Noise Analysis of Photonic Modulator Neurons
Noise Analysis of Photonic Modulator Neurons
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DOI：
10.1109/jstqe.2019.2931252
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发表时间：
2020-01-01
影响因子：
4.9
通讯作者：
Prucnal, Paul R.
中科院分区：
文献类型：
--
作者：
de Lima, Thomas Ferreira;Tait, Alexander N.;Prucnal, Paul R.
神经形态光子学依赖于高速有效地仿真模拟神经网络。先前的工作表明，将信号从光域转换到电域并利用跨谐增益进行黑客攻击是实现模拟光子神经元和可扩展网络的有效方法。在这里，我们研究了基于调制器的光子神经元电路与被动和主动transimsimpler增益，特别注意的噪声传播的来源。我们发现，调制器的非线性传递函数可以抑制噪声，这是必要的，以避免噪声传播的硬件神经网络。此外，虽然高效的调制器可以降低单个神经元的功率，但信噪比必须与系统级的功耗进行权衡。有源互阻抗放大器可有助于放宽常规p-n结硅光子调制器的此折衷，但当非常有效的调制器（即，低C和低V-pi）。
Neuromorphic photonics relies on efficiently emulating analog neural networks at high speeds. Prior work showed that transducing signals from the optical to the electrical domain and hack with transimpedance gain was an efficient approach to implementing analog photonic neurons and scalable networks. Here, we examine modulator-based photonic neuron circuits with passive and active transimpedance gains, with special attention to the sources of noise propagation. We find that a modulator nonlinear transfer function can suppress noise, which is necessary to avoid noise propagation in hardware neural networks. In addition, while efficient modulators can reduce power for an individual neuron, signal-to-noise ratios must be traded off with power consumption at a system level. Active transimpedance amplifiers may help relax this tradeoff for conventional p-n junction silicon photonic modulators, but a passive transimpedance circuit is sufficient when very efficient modulators (i.e., low C and low V-pi) are employed.