Parallel Video Processing Neural Network Architecture
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Solution Overview
Problem
Existing video processing neural networks require sequential processing, leading to high computational requirements and latency, which is inefficient and not suitable for real-time applications.
Innovation Solution
A video processing neural network architecture that allows parallelization of operations across multiple layer blocks at each time step, enabling parallel processing of video frames and reducing latency through diagonal connections and skip connections, while maintaining high prediction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sequential processing is used in existing video processing neural networks, then processing simplicity is maintained, but computational requirements increase and latency becomes high
Solution Approach 1:
The neural network is divided into multiple independent layer blocks that can process different video frames simultaneously. Each layer block operates on a specific time step, creating parallel processing paths that reduce overall computational requirements while maintaining processing depth.
Solution Approach 2:
The patent introduces a new dimension of parallelization by processing multiple time steps simultaneously across different layer blocks. This transforms the traditional single-sequence processing into a multi-dimensional parallel architecture where spatial and temporal dimensions are both utilized for concurrent computation.
2Loss of time
If sequential processing is used in existing video processing neural networks, then architecture simplicity is maintained, but latency increases making real-time processing difficult
Solution Approach 1:
The network is segmented into discrete layer blocks, each handling specific processing tasks at specific time steps. This segmentation enables independent parallel execution of blocks, significantly reducing the time required to process complete video sequences while the modular structure maintains manageable complexity.
Solution Approach 2:
Multiple layer blocks perform preliminary processing of different video frames in parallel before final aggregation. This preliminary parallel action reduces the critical path length of computation, thereby reducing latency without requiring complex sequential coordination.
3Productivity
If parallel processing is implemented, then processing speed and efficiency improve, but computational complexity increases
Solution Approach 1:
By segmenting the network into standardized layer blocks with uniform interfaces, the patent enables parallel processing while controlling complexity through modularity. Each block can be independently designed and optimized, and the segmented structure allows for systematic expansion without exponential complexity growth.
Solution Approach 2:
The layer blocks are designed with universal functionality, where each block can perform the same type of processing operation on different input data. This universality reduces overall system complexity by reusing the same computational primitives across multiple parallel instances, rather than requiring specialized blocks for each function.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallel processing of video frames using neural networks. One of the methods includes receiving a video sequence comprising a respective video frame at each of a plurality of time steps; and processing the video sequence using a video processing neural network to generate a video processing output for the video sequence, wherein the video processing neural network includes a sequence of network components, wherein the network components comprise a plurality of layer blocks each comprising one or more neural network layers, wherein each component is active for a respective subset of the plurality of time steps, and wherein each layer block is configured to, at each time step at which the layer block is active, receive an input generated at a previous time step and to process the input to generate a block output.