Optical Machine Learning System for Efficient Gradient Computation
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Solution Overview
Problem
Machine-learning algorithms require extensive datasets and computing power to generate models with sufficient accuracy, which can be resource-intensive and inefficient.
Innovation Solution
A system and method utilizing an optical source, adjustable spatial light modulator, and optical detector to generate synthetic gradients for updating electronic models, leveraging optical signals transmitted through a medium to determine error data and update machine-learning models efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine-learning algorithms are used to train models, then model accuracy can be achieved, but extensive computing power and resources are required
Solution Approach 1:
The patent replaces traditional electronic computing systems with an optical computing system that uses light propagation through a medium (such as a diffusive medium or photonic circuit) to perform matrix operations for training machine learning models. The optical system uses spatial light modulators to encode input data and control signals, and detectors to measure output signals, thereby computing gradients and updating model parameters optically instead of electronically, significantly reducing power consumption while maintaining model accuracy.
2Measurement precision
If traditional machine-learning algorithms are used to train models, then sufficient model accuracy can be generated, but extensive datasets and time are required
Solution Approach 1:
The patent replaces sequential electronic computation with parallel optical computation. The optical system can process multiple data points and compute gradients simultaneously through light propagation, eliminating the sequential bottlenecks of traditional electronic processors. This parallel processing capability dramatically reduces training time while achieving the same model accuracy, as the optical operations occur at the speed of light rather than electronic clock cycles.
3Use of energy by moving object
If optical computing is used to determine synthetic gradients, then computational resources are reduced, but system complexity increases
Solution Approach 1:
The patent designs the optical system with multi-functional components that can perform multiple operations. The spatial light modulator can encode different types of data (input features, gradients, control signals) and the optical medium can perform various matrix operations (multiplication, addition, transposition) depending on the configuration. This universality reduces the need for separate dedicated components for each function, thereby managing system complexity while achieving significant computational resource savings.
Solution Approach 2:
The patent uses optical copies of data encoded in light fields rather than physical electronic data storage and manipulation. Input data, gradients, and model parameters are represented as optical patterns that can be transmitted, transformed, and measured without physical movement or copying of material substrates. This optical copying mechanism reduces the complexity associated with electronic data handling and storage while maintaining computational efficiency.
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 enables faster and more efficient determination of synthetic gradients for deep neural networks, reducing the computational resources needed for model training and improving accuracy.
Implementation Method 1
an optical source and an adjustable spatial light modulator coupled to the optical source. The system further includes a medium coupled to the adjustable spatial light modulator, and an optical detector coupled to the medium. The optical detector obtains various optical signals that are transmitted through the medium
Data Source
AI summary
A system may include an optical source and an adjustable spatial light modulator coupled to the optical source. The system may further include a medium coupled to the adjustable spatial light modulator, and an optical detector coupled to the medium. The optical detector may obtain various optical signals that are transmitted through the medium at various predetermined spatial light modulations using the adjustable spatial light modulator. The system may further include a controller coupled to the optical detector and the adjustable spatial light modulator. The controller may train an electronic model using various synthetic gradients based on the optical signals.