Multi-core Neural Rendering Acceleration via Position Encoding
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Solution Overview
Problem
Conventional neural radiance field-based image rendering techniques face challenges in achieving real-time image rendering due to the large amount of data that needs to be processed, making them impractical for real-time applications.
Innovation Solution
A computing system comprising multiple computing cores that utilize position encoding logic and pipeline logics in series, capable of transforming coordinates and directions into high-dimensional representations, and executing computations associated with neural network layers in parallel to output intensity and color values of pixels, leveraging Fourier feature mapping and synchronous random access memory for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural radiance field-based image rendering techniques are used, then image rendering quality can be achieved, but real-time rendering speed cannot be achieved due to large amount of data processing required
Solution Approach 1:
The patent divides the image rendering task into multiple segments by splitting the image into multiple regions or tiles, each processed by separate computing cores. This segmentation allows parallel processing of different image portions simultaneously, significantly improving rendering speed while maintaining quality through distributed computation across multiple cores
Solution Approach 2:
The patent transforms the rendering problem by introducing high-dimensional representations of coordinates and directions through position encoding logic. This dimensionality transformation enables the neural network to process spatial information more efficiently in a higher-dimensional space, achieving both quality preservation and speed improvement through enhanced feature representation
2Productivity
If position encoding logic transforms coordinates and directions into high dimensional representations, then data processing efficiency is improved, but computational complexity increases
Solution Approach 1:
The position encoding logic performs preliminary transformation of coordinates and directions into high-dimensional representations before the main rendering computation. This preprocessing step organizes spatial information in advance, making subsequent neural network processing more efficient despite the increased dimensional complexity
Solution Approach 2:
The high-dimensional position encoding acts as an intermediary representation between raw coordinate data and the neural network processing. This intermediate representation facilitates more efficient computation by providing structured spatial features that the rendering network can process more effectively
Data Source
AI summary
A computing core for rendering an image computing core comprises a position encoding logic and a plurality of pipeline logics connected in series in a pipeline. The position encoding logic is configured to transform coordinates and directions of sampling points corresponding to a portion of the image into high dimensional representations. The plurality of pipeline logics are configured to output, based on the high dimensional representation of the coordinates and the high dimensional representation of the directions, intensity and color values of pixels corresponding to the portion of the image in one pipeline cycle. The plurality of pipeline logics are configured to run in parallel.