Optical Neural Network for LiDAR Point Cloud Processing
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Solution Overview
Problem
Conventional LiDAR systems face challenges in processing large and unstructured 3D point clouds due to high computational complexity and energy inefficiency, particularly in real-time applications like autonomous driving, where current digital electronics-based processing units are costly and limited by time and energy efficiency.
Innovation Solution
Implementing a layered neural network on an analog computing platform that performs operations in the electronic and/or optical domain, minimizing the need for data converters and enabling efficient processing of LiDAR data by leveraging optical processing chips with a Broadcast-and-Weight architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital electronics-based processing units are used to process LiDAR point clouds, then processing capability is provided, but energy efficiency deteriorates and processing time increases
Solution Approach 1:
The patent replaces digital electronics-based processing with an optical computing system that uses light propagation and optical components (modulators, waveguides, photodetectors) to perform computations. This substitution of mechanical/electronic systems with optical systems enables parallel processing of point cloud data while significantly reducing energy consumption and processing time.
Solution Approach 2:
The patent transforms the 3D point cloud data into a 2D matrix representation that can be processed by the optical computing system. This dimensional transformation allows the unstructured 3D data to be compatible with the optical processing architecture, enabling efficient computation through matrix operations implemented via light propagation.
2Productivity
If digital electronics-based processing units are used to process LiDAR point clouds, then processing capability is provided, but processing time increases
Solution Approach 1:
The patent replaces sequential digital electronics processing with parallel optical computing. The optical system performs computations simultaneously across multiple data points using light propagation through optical networks, dramatically reducing processing time while maintaining processing capability.
Solution Approach 2:
The optical computing system enables continuous processing of point cloud data through uninterrupted light propagation. The optical signals continuously traverse the computational network, performing operations in parallel without the discrete switching and processing delays inherent in digital electronics, thereby reducing overall processing time.
3Loss of information
If point clouds are processed in unstructured 3D format, then geometric information is preserved, but computational complexity increases
Solution Approach 1:
The patent transforms unstructured 3D point cloud data into a structured 2D matrix format that preserves geometric relationships while enabling efficient optical processing. This dimensional transformation maintains the essential spatial information needed for LiDAR applications while making the data compatible with matrix-based optical computing operations.
Solution Approach 2:
The patent changes the representation parameters of point cloud data from unstructured 3D coordinates to structured 2D matrix elements. This parameter transformation allows the data to be processed using standard matrix operations in the optical domain, reducing computational complexity while preserving the geometric information through the mathematical transformation.
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 significantly improves energy and time efficiency, allowing for real-time processing of large LiDAR point clouds with enhanced compute density, overcoming the limitations of digital electronics in handling large datasets.
Implementation Method 1
at least one optical processing chip operative to optically perform multiply-and-accumulate (MAC) operations with the matrix elements in the analog domain
Data Source
AI summary
Layers of a neural network can be implemented on an analog computing platform operative to perform MAC operations in series or in parallel in order to cover all elements of an arbitrary size matrix. Embodiments include a convolutional layer, a fully-connected layer, a batch normalization layer, a max pooling layer, an average pooling layer, a ReLU function layer, a sigmoid function layer, as well as concatenations and combinations. Applications include point cloud processing, and in particular of the processing of point clouds obtained as part of a LiDAR application. To implement elements of point cloud data as optical intensities, the data can be linearly translated in order to be fully represented with non-negative values.