Optical Convolutional Computing Device Multi-Wavelength Interference Reduction
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Solution Overview
Problem
Existing optical convolutional computing devices using single-wavelength coherent light sources face challenges in parallel computations, leading to increased image sensor requirements or decreased accuracy due to interference when synthesizing computational results.
Innovation Solution
An optical convolutional computing device utilizing a multi-wavelength light source, spatial light modulators, optical demultiplexers, transform devices, kernel-based SLMs, inverse transform devices, and an optical multiplexer to perform convolutional computations optically, thereby reducing interference and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a single-wavelength coherent light source is used for parallel computations, then the computational speed is improved, but the accuracy of the computational result decreases due to interference when synthesizing results
Solution Approach 1:
The patent changes the wavelength parameter of the light source from single-wavelength to multi-wavelength. By using light sources with different wavelengths (e.g., different colors of light) for different computational channels, the system maintains high computational speed through parallel processing while eliminating interference issues during result synthesis, since each wavelength operates independently and can be cleanly separated by the optical demultiplexer.
Solution Approach 2:
The patent introduces an optical demultiplexer as an intermediary device that separates the multi-wavelength light into individual wavelength components. This intermediary enables the system to process multiple wavelengths in parallel while preventing interference during synthesis, as each wavelength component can be independently directed to its corresponding computational channel and then recombined without cross-interference.
2Measurement precision
If multiple image sensors are used to recognize each computational result of parallel computations, then the computational accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent makes the optical demultiplexer a universal component that handles multiple wavelengths through a single device. Instead of requiring separate image sensors for each wavelength channel, the optical demultiplexer universally processes all wavelength components, separating and directing them appropriately, thereby maintaining high computational accuracy while significantly reducing the number of image sensors needed.
Solution Approach 2:
The patent merges the functions of multiple image sensors into a single optical demultiplexer system. By combining the wavelength separation and signal routing functions into one component, the system achieves the same computational accuracy that would require multiple sensors, while reducing overall device complexity and the number of discrete components needed.
3Productivity
If parallel computations are performed using multiple kernels, then the productivity is improved, but the number of image sensors required increases
Solution Approach 1:
The patent changes the operational parameter from single-wavelength to multi-wavelength light sources, enabling each wavelength to carry a separate computational channel. This allows parallel computations with multiple kernels to be performed simultaneously using a single image sensor, as the optical demultiplexer separates the wavelength-multiplexed signals, thereby maintaining high productivity while reducing the quantity of image sensors required to one.
Solution Approach 2:
The patent adds the wavelength dimension to the computational system, transforming it from a single-channel (single wavelength) system to a multi-channel system where each wavelength represents an independent computational dimension. This dimensional expansion enables parallel processing of multiple kernels through wavelength division multiplexing, allowing the system to handle more computational tasks simultaneously while using the same hardware resources, thus improving productivity without increasing the number of image sensors.
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
The proposed solution enables efficient parallel computations in optical convolutional computing devices, reducing the need for multiple image sensors and minimizing interference, thus enhancing computation accuracy and energy efficiency.
Implementation Method 1
a multi-wavelength light generator that outputs multi-wavelength light having first to N-th wavelengths
Implementation Method 2
an image-based spatial light modulator (SLM) that receives spatial domain image data and modulates the multi-wavelength light based on the spatial domain image data
Implementation Method 3
an optical demultiplexer that outputs first to N-th sub-lights having the first to N-th wavelengths, respectively, based on the modulated light
Implementation Method 4
first to N-th transform devices that performs Fourier transform on the first to N-th sub-lights
Implementation Method 5
first to N-th kernel-based SLMs that respectively receives first to N-th kernel data, and respectively modulates the first to N-th transformed lights based on the first to N-th kernel data
Implementation Method 6
first to N-th inverse transform devices that respectively performs inverse Fourier transform on the first to N-th kernel product lights
Implementation Method 7
an optical multiplexer that matches paths of the first to N-th inversely transformed lights using at least one optical device, and outputs synthesized light
Data Source
AI summary
An optical convolutional computing device includes an image-based spatial light modulator (SLM) configured to modulate a multi-wavelength light based on aspatial domain image data, and output modulated light; an optical demultiplexer configured to output first to N-th sub-lights having first to N-th wavelengths, respectively, based on the modulated light; an optical convolution processor configured to receive the first to N-th sub-lights and output first to N-th inversely transformed lights; and an optical multiplexer configured to align paths of the first to N-th inversely transformed lights, wherein N is a natural number greater than or equal to 2.


