Adjustable Optical Metamaterials for Reconfigurable CNN Convolution
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Solution Overview
Problem
Convolutional neural networks require intense computational power, making them difficult to implement in mobile environments due to high power consumption, and existing optical computing approaches like Mach-Zehnder interferometers and electro-optic modulators are either bulky, power-hungry, or limited by bandwidth and linewidth, failing to efficiently perform kernel reconfiguration.
Innovation Solution
The use of optical metamaterials with photoelectric detector arrays and nonlinear electronic circuitry, along with a feedback loop structure, to compute convolutions by transforming input vectors and kernels through Fourier transforms, allowing for efficient and reconfigurable optical computation of convolutional neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If conventional digital electronic approaches (CPUs, GPUs, FPGAs, ASICs) are used to implement convolutional neural networks, then processing power and computational capability are improved, but power consumption increases dramatically (up to 99% of overall computational power consumption)
Solution Approach 1:
The patent replaces digital electronic computation with optical computation. Instead of using electronic circuits (CPUs, GPUs, FPGAs, ASICs) to perform convolution operations, the invention uses optical components including spatial light modulators, Fourier lenses, and photodetector arrays to perform the same computational tasks optically, thereby dramatically reducing power consumption while maintaining computational capability.
Solution Approach 2:
The patent employs time-division multiplexing where the optical system performs sequential operations: encoding input vectors, performing Fourier transforms, multiplying by kernel transforms, and detecting outputs in alternating time slots. This periodic action allows a single optical system to handle multiple convolution operations efficiently, reducing overall power consumption compared to continuous electronic processing.
2Use of energy by moving object
If optical neuromorphic computers are used to perform convolutions, then energy consumption is reduced to O(N) and computational speed is increased, but device complexity and bulk increase due to requirements for multiple interferometers and modulators
Solution Approach 1:
The patent makes the optical system reconfigurable through programmable spatial light modulators that can be programmed with different input vectors and kernel transforms. This universality allows a single fixed optical architecture to perform multiple different convolution operations that would otherwise require separate dedicated hardware, significantly reducing device complexity while maintaining the energy efficiency of optical computation.
Solution Approach 2:
The patent introduces dynamic reconfigurability through spatial light modulators that can change their optical properties (phase, amplitude, or both) based on input data. This dynamic capability allows the same physical hardware to adapt to different computational tasks, eliminating the need for multiple fixed interferometer configurations and reducing overall system complexity.
3Productivity
If frequency division multiplexing is used in waveguide loops to address multiple neurons simultaneously, then computational efficiency is improved, but the number of inputs is severely limited by waveguide bandwidth and laser linewidth
Solution Approach 1:
The patent transitions from frequency-domain multiplexing (limited by laser linewidth) to spatial-domain parallel processing. By using a two-dimensional array of spatial light modulators and a corresponding two-dimensional photodetector array, the system can process multiple input vectors simultaneously in the spatial domain, dramatically increasing the number of supported inputs beyond what frequency division multiplexing allows.
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 reduces power consumption and increases computational speed by leveraging optical metamaterials to perform convolutions with O(N) energy consumption, enabling efficient computation of convolutional neural networks in mobile environments.
Implementation Method 1
a first adjustable optical element illuminated with optical radiation comprising a feed wave, the first adjustable optical element being configured to transform the feed wave into a first wave corresponding to the input vector
Implementation Method 2
refracting optical radiation comprising the first wave with a first refractive optic to transform the first wave to a second wave corresponding to a Fourier transform F of the input vector
Implementation Method 3
a second adjustable optical element illuminated with optical radiation comprising the second wave, the second adjustable optical element being configured to transform the second wave to a third wave corresponding to a kernel multiplication G=KF in Fourier space
Implementation Method 4
refracting optical radiation comprising the third wave with a second refractive optic to provide a fourth wave corresponding to an inverse Fourier transform g of the kernel multiplication
Implementation Method 5
detecting optical radiation comprising the fourth wave with a detector array
Data Source
AI summary
Opto-electronic devices can evaluate convolutional neural networks with reduced power consumption and higher speeds using optical metamaterial structures. Methods and systems for convolution of an input vector f with a kernel k can include a first optical element that is adjustable according to the input vector f and a second optical element that is adjustable according to the kernel k, where either or both elements can include adjustable optical metasurfaces. In some approaches, the second optical element is adjustable according to a Fourier transform of the kernel k and is interposed between first and second lenses or volumetric metamaterials implementing Fourier and inverse Fourier transforms, respectively.