Neural Network Bottleneck Blocks Using Space-to-Depth Convolution
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Solution Overview
Problem
Existing neural network bottleneck architectures impose significant computational overhead and inefficiencies, particularly due to large spatial dimensions requiring numerous multiply-accumulate operations and large kernel sizes, which negatively impact model performance and computational resources.
Innovation Solution
Implementing space-to-depth and depth-to-space tensor transformations in bottleneck blocks to transfer computations from spatial to channel dimensions, reducing spatial dimensions while increasing channel dimensions, thereby optimizing depthwise convolutions and minimizing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional bottleneck architectures are used, then model processing is performed, but computational overhead is significant and efficiency is reduced
Solution Approach 1:
The patent applies space-to-depth tensor transformation to convert spatial dimensions (height and width) into channel dimensions, and depth-to-space transformation to convert channel dimensions back to spatial dimensions. This dimensionality transformation allows the model to process data with smaller spatial dimensions while maintaining the same number of elements, thereby reducing computational overhead in bottleneck blocks while preserving processing efficiency
Solution Approach 2:
The patent changes the dimensional parameters of tensors by applying space-to-depth transformation that converts a tensor of size (N, H, W) into (N, H×W, 1), effectively transforming the parameter distribution from spatial to channel dimension. This parameter change reduces the spatial dimensionality that contributes most to computational complexity, thereby reducing computational overhead while maintaining productivity
2Reliability
If large spatial dimensions are used in bottleneck blocks, then more features are preserved, but numerous multiply-accumulate operations are required increasing computational cost
Solution Approach 1:
The patent transforms the data representation from spatial dimensions to channel dimensions using space-to-depth transformation, allowing the model to maintain feature information in the channel dimension rather than requiring large spatial dimensions. This reduces the number of multiply-accumulate operations needed while preserving model performance through the transformed dimensional representation
3Measurement precision
If large kernel sizes are used for convolutions, then better feature extraction is achieved, but computational resources increase significantly
Solution Approach 1:
The patent applies space-to-depth transformation to convert spatial dimensions into channel dimensions before convolution operations. This transformation allows the use of smaller kernel sizes in the transformed space while achieving equivalent or better feature extraction, thereby reducing computational resources and energy consumption without sacrificing measurement precision
4Loss of energy
If bottleneck blocks reduce tensor size, then computational resources are reduced, but processing latency may increase due to transformation overhead
Solution Approach 1:
The patent applies space-to-depth transformation as a preliminary action before the main convolution operations in bottleneck blocks. By pre-transforming the tensor to a more efficient representation, subsequent operations require fewer computational resources, and the overall processing time is reduced despite the added transformation step
Solution Approach 2:
The dimensionality transformation reorganizes data in a way that enables more efficient computation in the transformed space, reducing the overall computational burden and processing latency despite the additional transformation operations
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for efficient bottleneck processing via dimensionality transformation. The techniques include receiving a tensor, and processing the tensor in a bottleneck block in a neural network model, comprising applying a space-to-depth tensor transformation, applying a depthwise convolution, and applying a depth-to-space tensor transformation.