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US-20260127424-A1 - PHOTONIC KOLMOGOROV-ARNOLD NETWORK

US20260127424A1US 20260127424 A1US20260127424 A1US 20260127424A1US-20260127424-A1

Abstract

Systems and methods are provided for optically implemented Kolmogorov-Arnold Networks (KAN). Examples provide a photonic Kolmogorov-Arnold Network that includes a plurality of neurons and a plurality of synaptic edges. Each synaptic edge comprises a waveguide that optically couples a neuron of the plurality to another neuron of the plurality of neurons, and a nonlinear optical modulator formed on the waveguide, wherein the nonlinear optical modulator is configured to be tuned to a desired nonlinear activation function.

Inventors

  • Sean Hooten
  • Yiwei Peng
  • Yuan Yuan
  • Stanley Cheung

Assignees

  • HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Dates

Publication Date
20260507
Application Date
20241216

Claims (20)

  1. 1 . A photonic artificial neural network comprising: a plurality of neurons; a plurality of synaptic edges, wherein each synaptic edge comprises: a waveguide that optically couples a neuron of the plurality of neurons to another neuron of the plurality of neurons; and a nonlinear optical modulator formed on the waveguide, wherein the nonlinear optical modulator is configured to be tuned to a desired nonlinear activation function.
  2. 2 . The photonic artificial neural network of claim 1 , the plurality of neurons are organized into a plurality of layers, wherein the plurality of layers comprises an input layer comprising a first subset of neurons of the plurality of neurons and a hidden layer comprising a second subset of neurons of the plurality of layers.
  3. 3 . The photonic artificial neural network of claim 2 , wherein the first subset of neurons comprises one or more optical splitters configured to split one or more input optical signals amongst a first set of waveguides corresponding to a first subset of synaptic edges of the plurality of synaptic edges, wherein the first set of waveguides optically couple the first subset of neurons to the second subset of neurons.
  4. 4 . The photonic artificial neural network of claim 3 , wherein the second subset of neurons comprises one or more optical combiners configured to combine optical signals propagating on the first set of waveguides, wherein the combined optical signals are based on the tuning of the nonlinear optical modulators.
  5. 5 . The photonic artificial neural network of claim 3 , wherein the first subset of neurons comprises a first neuron and a second neuron, and wherein the second subset of neurons are connected to the first neuron by a first subset of waveguides of the first set of waveguides and are connected to the second neuron by a second subset of waveguides of the first set of waveguides, wherein each of the second subset of neurons comprises one or more optical combiners configured to combine optical signals propagating on the first subset of waveguides with optical signals propagating on the second subset of waveguides.
  6. 6 . The photonic artificial neural network of claim 3 , wherein the optical signals propagating on the first and second subsets of waveguides are based on the tuning of the nonlinear optical modulators formed on each respective waveguide.
  7. 7 . The photonic artificial neural network of claim 1 , wherein the optical modulator comprises a directional coupler and an interferometer.
  8. 8 . The photonic artificial neural network of claim 7 , wherein the directional coupler comprises a first phase-shift mechanism.
  9. 9 . The photonic artificial neural network of claim 7 , wherein the interferometer is a ring assisted interferometer comprising a microring optically coupled to a first branch of the interferometer and a second phase-shift mechanism coupled to a second branch of the interferometer.
  10. 10 . The photonic artificial neural network of claim 1 , wherein the optical modulator comprises a first Mach-Zehnder coupler connected to a first ring-assisted Mach-Zehnder interferometer, a second Mach-Zehnder coupler connected to a second ring-assisted Mach-Zehnder interferometer, and an optical amplifier provided between the first ring-assisted Mach-Zehnder interferometer and the second Mach-Zehnder coupler.
  11. 11 . The photonic artificial neural network of claim 1 , wherein the photonic artificial neural network comprises a Kolmogorov-Arnold Network.
  12. 12 . An optical device, comprising: a plurality of sources to emit a plurality of input optical signals; a plurality of optical splitters to split the plurality of input optical signal into a plurality of first optical signals; a plurality of waveguides optically coupled to the plurality of optical splitters, wherein the plurality of waveguides receive the plurality of first optical signals from the plurality of optical splitters; a plurality of optical combiners optically coupled to the plurality of waveguides; and a plurality of nonlinear optical modulators formed on the plurality of waveguides between the plurality of optical splitters and the plurality of optical combiners, wherein the plurality of nonlinear optical modulators are tunable to select nonlinear activation functions from a plurality of nonlinear activation functions, wherein the plurality of nonlinear optical modulators apply the selected nonlinear activation functions to the plurality of first optical signals to generate a plurality of second optical signals, wherein each of the plurality of optical combiners receives a subset of the plurality of second optical signals.
  13. 13 . The optical device of claim 12 , wherein the plurality of optical splitters represent neurons of a photonic artificial neural network.
  14. 14 . The optical device of claim 13 , wherein the plurality of waveguides represent network edges of the photonic artificial neural network.
  15. 15 . The optical device of claim 13 , wherein the photonic artificial neural network is a Kolmogorov-Arnold Network.
  16. 16 . The optical device of claim 12 , wherein the plurality of waveguides comprises: a first subset of waveguides optically coupled to a first optical splitter of the plurality of optical splitters; and a second subset of waveguides optically coupled to a second optical splitter of the plurality of optical splitters, wherein each of the plurality of optical combiners is optically coupled to a waveguide of the first subset of waveguides and a waveguide of the second subset of waveguides.
  17. 17 . The optical device of claim 12 , wherein each of the nonlinear optical modulators comprises: at least one microring-assisted Mach-Zehnder interferometer; and at least one Mach-Zehnder coupler connected to the microring-assisted Mach-Zehnder interferometer.
  18. 18 . The optical device of claim 12 , wherein each of the nonlinear optical modulators comprises a dual microring-assisted Mach-Zehnder interferometer.
  19. 19 . A method, comprising: supplying a plurality of first optical signals to a first layer of a Kolmogorov-Arnold Network (KAN), wherein the first optical signals are encoded with input data; splitting, by the first layer of the KAN, the plurality of first optical signals into a first set of waveguides and a second set of waveguides, wherein the first and second sets of waveguides represent network edges connecting the first layer to a hidden layer of the KAN, wherein each of the first and second sets of waveguides comprises a nonlinear optical modulator; applying nonlinear weights to the plurality of first optical signals based on tuning the nonlinear optical modulators to generate a plurality of weighted optical signals that propagate on the first and second sets of waveguides; summing, at the hidden layer of the KAN, subsets of the plurality of weighted optical signals; and detecting, by a photodetector, an output optical power that is based on the summing of the subset of the plurality of weight optical signals; and classifying the input data samples according to a class based on the detected output optical power.
  20. 20 . The method of claim 19 , wherein each of the nonlinear optical modulators comprises: at least one Mach-Zehnder interferometer comprising a microring and a first phase-shift mechanism; and at least one Mach-Zehnder coupler connected to the Mach-Zehnder interferometer and comprising a second phase-shift mechanism, and wherein tuning the nonlinear optical modulators comprises adjusting one or more of: a phase of the microring, optical loss of the microring, a phase in the Mach-Zehnder interferometer based on the first phase-shift mechanism, and a phase in the Mach-Zehnder coupler based on the second phase-shift mechanism.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63/716,031, filed on Nov. 4, 2024, the contents of which are incorporated herein by reference in their entirety. BACKGROUND Driven by growing interest in artificial intelligence (AI), the global artificial neural network (ANN) market is projected to grow at a significant rate. ANNs and learning algorithms have the ability to learn from large data sets, which can create a machine having human-like decision making capabilities with low latency and high-energy efficiency. ANNs are computing systems inspired by biological neural networks, and consist of a collection of connected nodes or neurons that are connected by edges, which model synapses. Each neuron can receives signals from connected neurons, processes the received signals, and sends a signal to connected neurons. The output of each neuron is computed by a non-linear activation function of the sum of its inputs, called the activation function. The strength of the output at each connection can be determined by a weight, which adjusts during a learning process. BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure, in accordance with one or more various examples, is described in detail with reference to the following figures. The figures are provided for purposes of illustration only and merely depict typical, non-limiting aspects of such examples. FIG. 1 depicts a schematic diagram of an optical device for an all-optical artificial neural network, in accordance with examples of the disclosed technology. FIG. 2 illustrates example nonlinear activation functions that can be implemented along edges of the all-optical artificial neural network of FIG. 1. FIG. 3 depicts a schematic diagram of a modulating unit cell, in accordance with implementations disclosed herein. FIG. 4 depicts a schematic diagram of an example an optical modulator comprising a plurality of modulating unit cells of FIG. 3, in accordance with implementations disclosed herein. FIG. 5 illustrates a graphical representation of accuracy and loss of an example all-optical artificial neural network according to one implementation disclosed herein. FIGS. 6A and 6B illustrates a graphically comparison of performance an all-optical artificial neural network in accordance with the present disclosure against some conventional artificial neural networks. FIG. 7 illustrates a schematic representation of pruning all-optical artificial neural network in accordance with an implementation of the present disclosure. FIG. 8 is an example computing component that may be used to implement various features of photonic Kolmogorov-Arnold Networks in accordance with the implementations disclosed herein. FIG. 9 is a computing component that may be used to implement examples of the disclosed technology. The figures are not exhaustive and do not limit the present disclosure to the precise form disclosed. DETAILED DESCRIPTION Examples of the technology disclosed herein provide for optically implemented Kolmogorov-Arnold Networks (KAN). Examples integrate an all-optical neuromorphic platform that leverages optical nonlinear activation functions (ONAFs) along synaptic edges interconnecting neurons of the KAN. The ONAFs can be implemented using optical modulators, such as, in some examples, cascaded ring-assisted Mach-Zehnder Interferometer (MZI) devices. Some ANNs are implemented using Multi-Layer Perceptrons (MLPs). MLPs are fully-connected feedforward neural networks that consist of multiple layers of nodes. Each node can process information by applying a fixed activation function to a weighted sum of its inputs. MLPs can theoretically approximate any continuous function, given enough layers and nodes. This capability allows for widespread use in diverse deep learning tasks, such as classification, regression, and processing of natural language. While versatile, MLPs may also have limitations, including challenges in interpreting learned representations and difficulties in scaling the network effectively. KANs present an alternative to traditional MLPs. One distinction between KANs and MLPs is in a KAN's ability to learn activation functions on the edges that interconnect nodes. As a result, edges of a KAN can implement weights using learned activation functions. In some cases, KANs can be trained to implement nonlinear activation functions (NAFs) on the edges, which can enable nonlinear weighting of outputs from connected nodes. This is in contrast to the weight-static approach offered by MLPs, thereby offering greater flexibility during training. Certain KAN models can utilize B-splines to construct activation functions, replacing linear weight parameters with adaptable spline-based functions. The advantages of this approach, such as, but not limited to, scalability, flexibility, efficiency, and interpretability, have spurred further investigation into the potential of KANs. Gen