Asymmetric Convolution-Attention Neural Networks for Faster Inference
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Solution Overview
Problem
Existing hybrid architectures for computer vision tasks, such as those combining convolutional and transformer blocks, face limitations in achieving a balanced trade-off between latency and performance due to symmetric distribution of layers, leading to compromised model performance and inefficient use of hardware accelerators.
Innovation Solution
An asymmetrically distributed convolution-attention neural network (AsCAN) design, where more convolutional blocks are used in early stages with large spatial feature maps and more transformer blocks in later stages, leveraging a simple hybrid architecture to improve throughput and maintain high performance without additional computation optimizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If symmetric distribution of convolutional and transformer blocks is used, then architectural simplicity is maintained, but throughput and performance trade-off is compromised
Solution Approach 1:
The patent applies asymmetry by distributing convolutional and transformer blocks unevenly across network stages. Early stages use more convolutional blocks to handle large spatial feature maps efficiently, while later stages use more transformer blocks to capture global dependencies when spatial dimensions are reduced. This asymmetric distribution optimizes the throughput-performance trade-off by matching computational operations to the characteristics of feature maps at different processing depths.
Solution Approach 2:
The patent implements local quality by assigning different types of blocks to different stages based on the local characteristics of feature maps. In early stages where spatial resolution is high, convolutional blocks provide efficient local feature extraction. In later stages where global context becomes more important, transformer blocks provide adaptive global attention. This localized optimization of block types throughout the network improves overall throughput while maintaining performance.
2Reliability
If more transformer blocks are used to improve global receptive field, then model performance is enhanced, but computational complexity and latency increase
Solution Approach 1:
The patent segments the network into multiple stages with different block distributions, rather than uniformly applying transformer blocks throughout. This segmentation allows the model to progressively build global receptive field only when necessary in later stages, reducing overall computational complexity and inference latency while maintaining performance benefits where they are most needed.
Solution Approach 2:
The patent performs preliminary local feature extraction using convolutional blocks in early stages before applying transformer blocks. This preliminary action prepares the feature maps by extracting local patterns and reducing spatial dimensions, so that subsequent transformer operations can focus on global dependencies with reduced computational burden, thereby lowering inference latency.
3Loss of time
If more convolutional blocks are used to reduce computational complexity, then inference latency is reduced, but global receptive field capability is limited
Solution Approach 1:
The patent merges convolutional and transformer blocks within a hybrid architecture, combining the efficient local feature extraction of convolutions with the global context capture of attention mechanisms. This merging allows the model to leverage both operation types synergistically, reducing inference latency through convolutions while maintaining global receptive field capability through strategically placed transformer blocks.
Data Source
AI summary
An asymmetrically distributed convolution-attention neural network (AsCAN) includes a simple hybrid architecture in which the number of convolutional and transformer blocks is asymmetrically distributed in different processing stages. AsCAN adopts more convolutional blocks in the early processing stages, where the feature maps have relatively large spatial sizes, and more transformer blocks at the later processing stages. Transformer layers are incorporated in the early processing stages as well, except that fewer transformer blocks are used compared to convolutions in the early part. This trend is reversed at the lower resolution in the later processing stages. This uneven distribution of the convolutional and transformer blocks yields better throughput due to improved accelerator utilization at various batch sizes during the inference stage.