Optical Neural Network for Real-Time Image Classification
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Solution Overview
Problem
Conventional artificial neural networks are limited by processor speed, making it difficult to classify high-resolution camera images and sensor data in real-time at frame rates higher than 10 Hz, which is essential for reliable environment detection in automatic driving applications.
Innovation Solution
The use of optical neuron components that employ nonlinear optical effects for processing, allowing for optical processing of data at the speed of light, including frequency conversion and nonlinear mapping, to enhance computing speed and enable real-time classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional electronic neural networks are used for classifying camera images, then the classification can be performed with existing processor technology, but the processing speed is limited to frame rates of less than 10 Hz, which is insufficient for real-time applications requiring 30 Hz frame rates
Solution Approach 1:
The patent replaces electronic processing components (processors, GPUs) with optical processing components (optical neurons, waveguides, modulators). This substitution enables data processing at the speed of light rather than being constrained by electronic processor speeds, thereby achieving the required 30 Hz frame rate for real-time camera image classification in autonomous driving applications.
2Measurement precision
If the resolution of camera images is increased to improve detection precision, then the quality of environment detection is enhanced, but the data load increases making real-time classification impossible or technically very complex with current processor speeds
Solution Approach 1:
The patent employs optical processing components including optical neurons, waveguides, and modulators to handle high-resolution camera images. The optical system processes image data at the speed of light, enabling real-time classification of high-resolution images (e.g., 8 megapixels) that would be computationally infeasible with conventional electronic processors, thus maintaining both high detection precision and real-time processing capability.
3Reliability
If 360° 3D environment detection is implemented to improve reliability of automatic driving functions, then comprehensive environment detection is achieved, but the computing effort increases significantly requiring processing of many individual images
Solution Approach 1:
The patent uses an optical neural network system comprising multiple optical neurons, waveguides, and modulators to process the large volume of data from 360° 3D environment detection. The optical processing architecture handles the computing intensity of classifying multiple individual images from different cameras simultaneously, enabling comprehensive 360° detection with high reliability without being bottlenecked by electronic processing speeds.
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 the classification and processing of high-resolution camera images and sensor data in real-time, overcoming the limitations of conventional electronic neural networks by utilizing optical components for faster data processing.
Implementation Method 1
optical neuron components (220) for providing at least one neuron of the network, in particular as nonlinear optical components, in order to output an output signal (222) of the neuron component (220) having a second frequency, depending on an input signal (221) of the neuron component (220) having a first frequency, wherein the second frequency is different from the first frequency
Data Source
AI summary
The disclosure relates to a device for providing an artificial neural network, comprising at least one optical neuron component for providing at least one neuron of the network. The neuron component is in the form of a nonlinear optical component, in order to output, depending on an input signal of the neuron component having a first frequency, an output signal of the neuron component having a second frequency, wherein the second frequency is different from the first frequency.