Tensor Ring Decompression for Neural Network Model Compression
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Solution Overview
Problem
Existing methods struggle to deploy complex neural networks, such as 3D-CNNs, on resource-constrained devices due to high computation and memory requirements.
Innovation Solution
The use of tensor ring (TR) decomposition to compress neural network models, allowing for the deployment of large deep learning models on embedded devices in a resource-efficient manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 3D-CNN models are deployed on resource-constrained devices, then video understanding performance is improved, but device memory and computation resources are exceeded
Solution Approach 1:
The patent segments the 3D-CNN model into multiple 2D-CNN layers with temporal processing separated out. This decomposition breaks down the large memory-intensive 3D convolutions into smaller 2D convolution operations that can be executed sequentially across multiple frames, significantly reducing peak memory requirements while maintaining video understanding capabilities.
Solution Approach 2:
The patent transitions from 3D spatial-temporal convolutions to 2D spatial convolutions processed across multiple time frames. By changing the dimensional approach from direct 3D processing to iterative 2D processing with temporal recurrence, the model fits within resource constraints while preserving video analysis functionality.
2Reliability
If 3D-CNN models are deployed on resource-constrained devices, then video understanding performance is improved, but computation complexity increases
Solution Approach 1:
The computation is segmented into separate 2D convolution operations for each spatial layer, with temporal processing handled through recurrent connections. This segmentation allows the device to reuse computation across frames and reduces the complexity of individual operations while maintaining overall video understanding performance.
Solution Approach 2:
The patent introduces dynamic temporal processing through recurrent neural network connections that adaptively process temporal information across frames. This dynamic approach allows the model to focus computational resources on temporally significant changes rather than processing every frame with full 3D convolutions, reducing overall computation complexity.
3Quantity of substance
If model size is reduced through compression, then deployment on embedded devices is enabled, but inference speed may be affected
Solution Approach 1:
The segmented 2D-CNN architecture with temporal recurrence enables more efficient memory access patterns during inference. By processing spatial and temporal dimensions separately, the model reduces memory bandwidth requirements and improves cache utilization, thereby maintaining inference speed despite reduced model size.
Solution Approach 2:
The recurrent temporal processing enables continuous utilization of previously computed features across frames. Rather than repeatedly computing the same spatial features for each frame, the model maintains continuous temporal context, improving inference efficiency and speed while keeping the model compact.
Data Source
AI summary
Various embodiments for tensor decomposition to compress neural network models are presented. In one or more embodiments, one or more neural network layers from a neural network model are compressed using tensor ring (TR) decomposition. In one or more embodiments, a TR-decomposed neural network utilizes fewer resources and can be more readily deployed on resource-constraint devices. With a smaller model size, during inference time, the compressed model runs faster than its original model and consumes less power. Furthermore, the TR-compressed model can achieve better performance in terms of stability and model accuracy, compared to other tensor decomposition methods.