Photonic Advantage of Optical Encoders
This work addresses the problem of energy-efficient and low-latency AI applications, such as edge computing, by showing that optics can be advantageous when performance can be relaxed, but it is incremental as it builds on existing hybrid approaches without a breakthrough.
The study tackled the challenge of demonstrating a clear system-level advantage of optics over digital artificial neural networks (ANNs) by co-optimizing a hybrid optical-digital ANN that operates on incoherent light, identifying a low-power/latency regime where the optical encoder provides higher classification accuracy than purely digital ANNs, though with lower overall accuracy than higher-power alternatives.
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.