Light's ability to perform massive linear operations parallelly has recently inspired numerous demonstrations of optics-assisted artificial neural networks (ANN). However, a clear advantage of optics over purely digital ANN in a system-level has not yet been established. While linear operations can indeed be optically performed very efficiently, the lack of nonlinearity and signal regeneration require high-power, low-latency signal transduction between optics and electronics. Additionally, a large power is needed for the lasers and photodetectors, which are often neglected in the calculation of energy consumption. Here, instead of mapping traditional digital operations to optics, we co-optimized a hybrid optical-digital ANN, that operates on incoherent light, and thus amenable to operations under ambient light. Keeping the latency and power constant between purely digital ANN and hybrid optical-digital ANN, we identified a low-power/ latency regime, where an optical encoder provides higher classification accuracy than a purely digital ANN. However, in that regime, the overall classification accuracy is lower than what is achievable with higher power and latency. Our results indicate that optics can be advantageous over digital ANN in applications, where the overall performance of the ANN can be relaxed to prioritize lower power and latency.

翻译：光能够并行执行大规模线性运算的能力，近期催生了大量关于光学辅助人工神经网络（ANN）的演示。然而，在系统层面，光学相比纯数字ANN的明确优势尚未确立。尽管线性运算确实可以通过光学方式高效实现，但非线性特性与信号再生能力的缺失，要求光电器件间具备高功率、低延迟的信号转导。此外，激光器与光电探测器所需的大功率在能耗计算中常被忽略。本研究并未将传统数字运算映射至光学系统，而是对混合光-数字ANN进行协同优化，该系统基于非相干光工作，因此适用于环境光场景。在保持纯数字ANN与混合光-数字ANN延迟与功耗恒定的条件下，我们识别出一个低功耗/低延迟区间：在该区间内，光学编码器比纯数字ANN能提供更高的分类准确率。但此区间内整体分类准确率低于采用更高功耗与延迟时可达到的水平。我们的结果表明，在可适当降低ANN整体性能以优先实现更低功耗与延迟的应用场景中，光学相比数字ANN具有优势。