Programmable low-power consumption all-optical nonlinear activation functions using a micro-ring resonator with phase-change materials

Threshold plasticity of SOI-GST microring resonators

Spiking Neural Networks, also known as third generation Artificial Neural Networks, have widely attracted more attention because of their advantages of behaving more biologically interpretable and being more suitable for hardware implementation. Apart from using traditional synaptic plasticity, neural networks can also be based on threshold plasticity, achieving similar functionality. This can be implemented using e.g. the Bienenstock, Cooper and Munro rule. This is a classical unsupervised learning mechanism in which the threshold is closely related to the output of the post-synaptic neuron. We show in simulations that the threshold characteristics of the nonlinear effects of a microring resonator integrated with Ge2Sb2Te5 demonstrate some complex dependencies on the intracavity refractive index, attenuation, and wavelength detuning of the incident optical pulse, and exhibit class II excitability. We also show that we are able to modify the threshold power of the microring resonator by the changes of the refractive index and loss of Ge2Sb2Te5, due to transitions between the crystalline and amorphous states. Simulations show that the presented device exhibits both excitatory and inhibitory learning behavior, either lowering or raising the threshold.

All-optical spiking neural network and optical spike-time-dependent plasticity based on the self-pulsing effect within a micro-ring resonator

In this paper, we proposed an all-optical version of photonic spiking neurons and spike-time-dependent plasticity (STDP) based on the nonlinear optical effects within a micro-ring resonator. In this system, the self-pulsing effect was exploited to implement threshold control, and the equivalent pulse energy required for spiking, calculated by multiplying the input pulse power amplitude with its duration, was about 14.1 pJ. The positive performance of the neurons in the excitability and cascadability tests validated the feasibility of this scheme. Furthermore, two simulations were performed to demonstrate that such an all-optical spiking neural network incorporated with STDP could run stably on a stochastic topology. The essence of such an all-optical spiking neural network is a nonlinear spiking dynamical system that combines the advantages of photonics and spiking neural networks (SNNs), promising access to the high speed and lower consumption inherent to optical systems.

BP-based supervised learning algorithm for multilayer photonic spiking neural network and hardware implementation

We introduce a supervised learning algorithm for photonic spiking neural network (SNN) based on back propagation. For the supervised learning algorithm, the information is encoded into spike trains with different strength, and the SNN is trained according to different patterns composed of different spike numbers of the output neurons. Furthermore, the classification task is performed numerically and experimentally based on the supervised learning algorithm in the SNN. The SNN is composed of photonic spiking neuron based on vertical-cavity surface-emitting laser which is functionally similar to leaky-integrate and fire neuron. The results prove the demonstration of the algorithm implementation on hardware. To seek ultra-low power consumption and ultra-low delay, it is great significance to design and implement a hardware-friendly learning algorithm of photonic neural networks and realize hardware-algorithm collaborative computing.