Micro-ring Resonator Optical Neural Networks
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Solution Overview
Problem
Conventional electronic artificial neural networks (ANNs) face limitations such as high energy consumption, limited processing speed, and susceptibility to electromagnetic interference, which are exacerbated as they become larger and deeper, necessitating the development of more efficient and resilient computing hardware.
Innovation Solution
The implementation of optical neural networks (ONNs) using micro-ring resonators (MRRs) for fully-connected layers, which incorporate signal mixing and phase tuning components to perform linear transformations, reducing power consumption and increasing density while being resistant to electromagnetic interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional electronic ANNs are scaled up for more complicated tasks, then computational capability is improved, but energy consumption increases
Solution Approach 1:
The patent replaces electronic computing systems with optical computing systems using micro-ring resonators. The optical system performs matrix-vector multiplication through resonant coupling between adjacent MRRs, eliminating the need for electronic signal processing and reducing energy consumption while maintaining computational capability for neural network operations.
Solution Approach 2:
The patent changes the fundamental operating parameter from electronic signals to optical resonances. By tuning the resonant frequencies of MRRs and controlling coupling between them, the system achieves programmable linear transformations with lower power consumption compared to conventional electronic implementations.
2Productivity
If conventional electronic ANNs are scaled up for more complicated tasks, then computational capability is improved, but processing speed is limited
Solution Approach 1:
The patent substitutes electronic signal propagation with optical resonance and coupling mechanisms. The optical domain enables faster processing speeds because optical frequencies are inherently higher than electronic frequencies, allowing the system to perform matrix-vector multiplications more rapidly while handling complex neural network computations.
3Productivity
If conventional electronic ANNs are scaled up for more complicated tasks, then computational capability is improved, but susceptibility to electromagnetic interference increases
Solution Approach 1:
The patent replaces electronic systems that are inherently susceptible to electromagnetic interference with optical systems using micro-ring resonators. Optical signals in the resonators are immune to electromagnetic interference, providing robust and reliable computation for large-scale neural networks while maintaining the required computational capability.
4Use of energy by moving object
If optical neural networks using MRRs are implemented, then power consumption is reduced, but device complexity increases
Solution Approach 1:
The patent divides the computational function into modular unit cells, each containing a small number of MRRs that perform localized matrix-vector multiplication. This segmentation allows the complex overall computation to be distributed across many simple, identical modules, reducing the complexity of individual devices while achieving the required computational capability through parallel operation.
Solution Approach 2:
The patent designs universal unit cells with MRRs that can perform multiple functions: signal mixing, phase modulation, and resonant coupling. Each unit cell is programmable to implement different weight matrices, allowing a single hardware architecture to handle various neural network layers and operations, thereby reducing overall system complexity despite the advanced physics involved.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
ONNs based on MRRs achieve lower power consumption, higher processing speeds, and increased density compared to conventional approaches, enabling efficient and compact machine learning accelerators with reduced footprints and immunity to electromagnetic interference.
Implementation Method 1
A plurality of unit cells can be configured to operate on a plurality of wavelengths of light... each unit cell can comprise a plurality of sub-unit cells... Each sub-unit cell can include a signal mixing component and a phase tuning component
Implementation Method 2
The plurality of unit cells can be optically coupled between adjacent waveguides... Each unit cell can be configured to perform a linear transformation on a set of input signals according to a weight matrix
Data Source
AI summary
Systems and methods are provided for devices and methods for implementing an optical neural network (ONN) by leveraging resonator structures, such on micro-ring resonators (MRRs). Examples include unit cells configured to perform a linear transformation on optical signals. Each unit cell comprises a plurality of signal mixing components optically coupled to between adjacent waveguides, where each signal mixing component corresponds to a distinct wavelength and is configured to mix optical signals on the adjacent waveguides at the distinct wavelength. Each unit cell also includes a plurality of phase tuning components each corresponding to a distinct wavelength and configured to adjust a phase of a mixed optical signal at the distinct wavelength.