Distributed Recurrent Networks for High-Resolution Video Processing
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Solution Overview
Problem
Existing image and video processing operations using recurrent neural networks are computationally intensive and resource-constrained, particularly for high-resolution and high-frame-rate inputs, leading to inefficiencies and reduced accuracy in smartphones and edge computing devices.
Innovation Solution
Implementing a recurrent neural network architecture that distributes recurrent parameters across different time steps and utilizes efficient convolutions to compress recurrent states, along with simplified recurrent cell architectures and clipped activation functions, to accelerate and improve the efficiency of image and video processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recurrent neural networks are used for high-resolution image and video processing, then processing accuracy is improved, but computational complexity and resource consumption increase
Solution Approach 1:
The recurrent parameters are segmented and distributed across different time steps rather than being集中 at each step. This divides the computational load, allowing the system to maintain high processing accuracy while reducing the complexity burden at any single time step, making the network more suitable for resource-constrained devices
Solution Approach 2:
The patent changes the parameter distribution strategy by using distributed recurrent parameters across time steps and applying compression techniques to recurrent states. This parameter transformation reduces the overall computational complexity while preserving the accuracy benefits of recurrent processing for high-resolution inputs
2Measurement precision
If recurrent neural networks process high-frame-rate video inputs, then output quality is improved, but processing speed decreases
Solution Approach 1:
The recurrent parameters are pre-distributed across time steps during network configuration, and compression operations are prepared in advance. This preliminary arrangement enables faster real-time processing of high-frame-rate video while maintaining high output quality, as the computational structure is optimized before processing begins
Solution Approach 2:
By transforming the recurrent parameter structure and applying compression to recurrent states, the patent reduces the computational burden per frame, thereby increasing processing speed while preserving the high output quality necessary for high-frame-rate video processing
3Measurement precision
If recurrent parameters are concentrated at each time step, then processing accuracy is maintained, but resource consumption increases
Solution Approach 1:
The recurrent parameters are segmented across time steps, distributing the computational workload and reducing peak resource consumption. This segmentation maintains processing accuracy by preserving recurrent information flow while lowering the energy and memory demands at any given moment, making the system feasible for mobile and edge devices
Solution Approach 2:
The patent applies compression to recurrent states and redistributes parameters across time, fundamentally changing how resources are consumed. This parameter transformation reduces memory usage and computational energy while maintaining the accuracy benefits of recurrent processing
Data Source
AI summary
Disclosed are systems, apparatuses, processes, and computer-readable media for processing image data. For example, a process can include processing, for a first time step of a recurrent machine learning network, a first image of a plurality of images using a first subset of a group of distributed recurrent parameters to generate a first hidden state output associated with the first image. A process can include providing the first hidden state output as a recurrent state input to a second time step of the recurrent machine learning network. A process can include processing, for the second time step of the recurrent machine learning network, a second image of the plurality of images using the recurrent state input and a second subset of the group of distributed recurrent parameters to generate a second hidden state output associated with the second image.