Optical Neural Network Training with Amplitude-Phase Light Modulation
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Solution Overview
Problem
Traditional electronic computing methods for artificial intelligence and machine learning suffer from slow speed, high energy consumption, and poor scalability, necessitating the development of alternative computing paradigms like optical training that leverage the unique properties of light for faster and more efficient computation.
Innovation Solution
The method involves generating coherent light using a solid-state laser, splitting it into paths for amplitude and phase modulation using spatial light modulators and beam splitters, and measuring the output light beam to determine amplitude and phase, while the integrated photonic chip system controls light polarization and signal attenuation to achieve efficient training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional electronic computing methods are used for AI and machine learning training, then existing infrastructure can be utilized, but computation speed is slow and energy consumption is high
Solution Approach 1:
The patent replaces traditional electronic computing systems with optical computing systems. Specifically, it uses optical neural networks where light-based components (spatial light modulators, optical waveguides) perform computational tasks instead of electronic circuits. This substitution enables parallel optical processing that achieves faster computation speeds and lower energy consumption compared to sequential electronic processing
Solution Approach 2:
The patent changes the fundamental operating parameters of computation from electronic signals to optical signals. By transforming computational operations into optical domain operations (using light intensity, phase, and polarization for data representation and processing), the system achieves different performance characteristics including higher speed and efficiency in specific computational tasks
2Speed
If optical training is implemented to achieve faster computation, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent divides the optical computing system into distinct functional modules: light source units, spatial light modulators for weight modulation, optical waveguides for signal transmission, and detectors for output. This segmentation allows each component to be optimized independently and facilitates easier implementation and debugging while maintaining high processing speed
Solution Approach 2:
The patent introduces optical-to-electrical converters and electrical-to-optical converters as intermediary components that bridge the optical and electrical domains. These converters enable the integration of optical computing components with existing electronic control and measurement systems, reducing overall system complexity by providing standardized interfaces
3Productivity
If complex backpropagation is used in traditional training methods, then training can be performed, but training efficiency and stability are reduced
Solution Approach 1:
The patent replaces traditional electronic backpropagation algorithms with optical computing operations. By performing gradient calculations and weight updates using optical signal processing, the system achieves parallel computation of multiple gradients simultaneously, dramatically reducing training time and improving efficiency compared to sequential electronic computation
Solution Approach 2:
The patent implements continuous optical signal processing throughout the training process, eliminating the discrete iterative steps of traditional backpropagation. The optical system continuously computes and adjusts weights in real-time during forward propagation, maintaining useful computational action without interruption and reducing overall training time
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
This approach avoids complex backpropagation, enhances training efficiency and stability, and enables online training of deep photonic neural networks and optical imaging systems with high accuracy under resource constraints.
Implementation Method 1
generating coherent light of a preset wavelength by using a solid state laser
Implementation Method 2
expanding coherent light wavefront by using a beam expander
Implementation Method 3
inputting the first path light beam into a first spatial light modulator for data/error complex field loading
Implementation Method 4
the first spatial light modulator for field loading is configured to operate in an amplitude modulation mode
Implementation Method 5
relaying the amplitude-modulated light field to a second spatial light modulator through a 4F system for phase modulation
Implementation Method 6
passing the second path light beam through a half-wave plate and linear polarizer after polarization adjustment using the half-wave plate
Implementation Method 7
determining an amplitude and a phase of a beam measurement result by measuring the output light beam using a detecting symmetric propagation system
Data Source
AI summary
A method for online training of intelligent optical computing, applied to a free space system, includes: generating coherent light of a preset wavelength, and expanding coherent light wavefront using a beam expander, and splitting the coherent light wavefront into a first and second path light beams; inputting the first path light beam into a first spatial light modulator for data/error complex field loading and taking the second path light beam as an interfering light beam, the first spatial light modulator operating in an amplitude modulation mode; relaying the amplitude-modulated light field to a second spatial light modulator through a 4F system for phase modulation, and obtaining an output light beam by passing the second path light beam through a half-wave plate and linear polarizer after polarization adjustment using the half-wave plate; and determining an amplitude and a phase of a beam measurement result by measuring the output light beam.