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.

通过将光学与数字技术协同处理，并操作于凝聚态光学信号下，我们成功地优化了一个混合光学 - 数字的人工神经网络，发现相比于数字技术，其在低功耗/低延迟下具有更高的分类精度，这表明在一些应用中，光学技术比数字技术更优越。

光学编码器的光子优势