Current AI solutions are quite resources/energy-hungry and still time-consuming. In fact, today DNNs (as other AI models) still rely on Boolean algebra transistors to do an enormous amount of digital computations over huge data sets. The roadblock is that chipsets technologies aren’t getting faster at the same pace as AI software solutions are progressing in serving markets’ needs.
Will this energy consuming trend be really sustainable in the long term?
We remind that in basic functioning of a DNN, each high-level layer learns increasingly abstract higher-level features, providing a useful, and at times reduced, representation of the features to a lower-level layer. This similarity is suggestin, the intriguing possibility that DNNs principles are deeply rooted in Quantum Field Theory and Quantum Electromagnetics. This aspect is, perhaps, offering a way to bypass above roadblocks: developing AI technologies based on photonic/optical computing systems which are much faster and much less energy-consuming that current ones.
As a matter of fact, while, in line with the Moore’s law, electronics starts facing physically fundamental bottlenecks, nanophotonics technologies are considered promising candidates to overcome electronics future limitations. Consider that DNNs operations are mostly matrix multiplications, and nanophotonic circuits can make such operations almost at the speed of light and very efficiently due to the nature of photons. In simple words, photonic/optical computing uses electromagnetic signals (e.g., via laser beams) to store, transfer and process information. Optics has been around for decades, but until now, it has been mostly limited to laser transmission over optical fiber. Today technologies, using optical signals to do computations and store data, would accelerate AI computing by orders of magnitude in latency, throughput and power efficiency.
Matrix multiplication is the most power hungry and time-consuming operations in AI algorithms. Extrapolating current trends, speed of electronics components performing matrix calculations is likely to insufficient for supporting future AI applications, at least in the long term. Reducing the electric energy consumptions is also another strict requirement for sustainability.
Neuroscience may hold the solution. Our brain is not digital, it’s analogue, and it makes calculations all the time using electromagnetics signals, consuming just 30 W.
The advantage of using light processing to do matrix multiplication plays significantly in speeding up calculations and power savings. In fact, instead of using streams of electrons, the calculations are performed by beams of photons that interact with one another, in a medium, and with optical resonators and guiding components. To make it simple: unlike electrons, photons have no mass, travel at light-speed and draw no additional power once generated.
Interesting prototypes of an all-optical DNN are already available. For example, in this paper  shows the feasibility study of an all-optical diffractive DNN. The prototype is made of a set of diffractive layers, where each point (equivalent to a neuron) acts as a secondary source of an electromagnetic wave directed to the following layer.
The amplitude and phase of the secondary wave are determined by the product of the input wave and the complex-valued transmission or reflection coefficient at that point (following the laws of transformation optics). The transmission/reflection coefficient of each point of a layer is a learnable network parameter, which is iteratively adjusted during the training process (e.g., performed in a computer) using a classical error back-propagation method. After the training, the design of the layer is fixed as the transmission/reflection coefficients of all the neurons of all layers are determined.
Other examples of prototypes are based on the metamaterials, information meta-surface, and optical field-programmable gate array based on Mach-Zehnder Interferometers.
 Lin, Xing, et al. "All-optical machine learning using diffractive deep neural networks. Science 361.6406 (2018): 1004-1008.
More about this at the following link.