Interesting article from Democritus University which I'm not sure if it's been posted previously. We know they have worked with both Brainchip and NASA. Now they are looking into nanophotonic neural networks. They researched what has been done for multiple neural network types including CNN's and SNN's, This article was written by the same authors many on this forum will remember:
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In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides and other passive optical elements of nanostructured materials...
www.mdpi.com
Published: 18 January 2022
Here are some relevant / interesting parts:
4.5. Spiking Neural Networks
The spiking neural networks (SNNs) [
76,
77,
78] are networks that imitate more than any other the biological ΝΝs. Apart from the neural and synaptic condition, the SNNs incorporate the concept of time in their operating model. The idea behind this is that the neurons in a SNN should not trigger and be triggered in every propagation circle, as in standard networks of multiple layers with perceptron’s. As it happens with the biological neurons, when the dynamics of their cell membrane reaches a particular value, which is called action potential, then the neuron triggers and produces a signal that travels to other neurons, which, in turn, increase or decrease the dynamics of their cell membrane according to this particular signal. The SNNs use peak sequences as mechanisms of internal information presentation, in contrast to the usual continuous variables, while at the same time having equal, if not better, performance in computational cost to the traditional NNs [
79,
80,
81].
In the field of optical SNNs, many studies have been conducted in the past years [
82,
83], initially taking advantage of the fast optical elements used in the construction of big systems with optical fibers. Despite the significant advances to build active optical artificial neurons using for example phase-change materials, lasers, photodetectors and modulators, miniaturized integrated sources and detectors suited for few-photon spike-based operations and of interest for neuromorphic optical computing are still lacking. The successful applications finally led to the completion of arrangements, aiming for greater scalability, increase of energy efficiency, reduction of cost and flexibility in the environmental fluctuations.
In a survey, the use of a graphene laser is recommended as an artificial neuron, which is the fundamental element for the processing of information in the form of spikes. Moreover, the integrated layer of graphene is used as an optical absorber for the materialization of the non-linear activation function. The following
Figure 10 presents the application with the use of circuits of free optics for the creation of a series of current peaks with adjustable characteristics of width and breadth [
49,
82,
84,
85].
Figure 10. (
a) The circuit for the creation of repeated current peak. (
b) The waveforms of the implementation. One pulse of the output is led to the input via single-mode fiber (SMF), which acts as a delay element [
82].
In another survey, the fundamental neuron is based on distributed feedback (DFB) laser of semi-conductors of indium phosphide [
86]. The use of this type of laser devices is very common in the construction of SNNs. The laser possesses two photodetectors (PD), which allow for inhibitory as well as excitatory stimuli. The recommended device is very fast, reaching 1012 MACs/sec (MAC—Multiply Accumulate Operations) [
87,
88].
7. Conclusions
In this research paper, we present an overview of the development and materialization methods of
neuromorphic circuits of nanophotonic [
61] arrangements for every respective contemporary architecture of conventional neural networks, and the advantages and restrictions that arise during the transition from the electronic to the optical materializations are displayed. The aforementioned networks are energy efficient, when compared to the corresponding electronic ones, and much faster due to photons. The reduction of simultaneous processing time radically increases the potentials of modern computational systems, which use optical arrangements, offering a promising alternative approach to micro-electronic and optical-electronic applications.
All these lead to the conclusion that
there are potentials for a full transition to optical materializations as these display the following advantages:
(1)
Most of the systems do not require energy for the processing of optical signals. As soon as the neural network is trained, the computations on the optical signals are conducted without any additional energy consumption, rendering this particular architecture completely passive.
(2)
The optical systems, in contrast to the conventional electronic ones,
do not produce heat during their operation and, as a result, they can be enclosed in three-dimensional constructions.
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