Convolution as Transformer Engine for Mobile Latency Reduction
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Solution Overview
Problem
Deep learning machine learning models, particularly those using transformers, face high computational complexity and latency issues, especially on mobile devices, due to the need for extensive calculations and high memory demands, which limits their performance in tasks like object detection and classification.
Innovation Solution
The implementation of a Convolution as a Transformer (CAT) engine and a Self-Attention as Feature Fusion (SAFF) engine, which utilize depth-wise separable and pointwise convolutional filters to approximate transformer operations, reducing complexity from O(n2*d) to O(n*d) and leveraging self-attention for feature fusion across multiple scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformer operations are used for object detection and classification, then accuracy and versatility are improved, but computational complexity and latency increase significantly
Solution Approach 1:
The patent creates a simplified copy of transformer operations using convolutional neural networks. Instead of implementing full transformer self-attention mechanisms, the system uses CNN-based feature extraction and fusion that approximates transformer functionality with lower computational complexity, achieving similar detection accuracy with reduced O(n²*d) to O(n*d) complexity
Solution Approach 2:
The patent employs lighter-weight CNN architectures that are computationally cheaper and faster than transformers. By using efficient convolutional operations and depth-wise separable convolutions, the system achieves rapid feature extraction and processing suitable for mobile devices, sacrificing some transformer-specific capabilities for speed and efficiency
2Adaptability or versatility
If transformer operations are implemented on mobile devices, then object detection capability is improved, but processing speed and latency performance deteriorate
Solution Approach 1:
The patent replaces the mechanical computation of transformer self-attention with more efficient convolutional operations. By substituting the O(n²*d) transformer calculations with O(n*d) convolutional neural network operations, the system achieves significantly faster processing speeds while maintaining object detection capabilities on mobile devices
Solution Approach 2:
The patent changes the computational parameters by using depth-wise separable convolutions and pointwise convolutions that reduce the number of operations required. This parameter optimization allows the system to process images faster on mobile hardware while maintaining detection accuracy, reducing latency by over 50% compared to traditional transformer systems
3Measurement precision
If deep learning models with high accuracy are used, then detection performance is improved, but memory demands and computational costs increase
Solution Approach 1:
The patent segments the deep learning model into efficient convolutional blocks that process features in manageable stages. By using depth-wise separable convolutions that separate depth and spatial operations, the system reduces memory requirements while maintaining detection performance through hierarchical feature extraction at different scales
Data Source
AI summary
Systems and techniques are described herein for processing data (e.g., image data) using convolution as a transformer (CAT) operations. The method includes receiving, at a convolution engine of a machine learning system, a first set of features, the first set of features being associated with an image and having a three-dimensional shape, applying, via the convolution engine, a depth-wise separable convolutional filter to the first set of features to generate a first output, applying, via the convolution engine, a pointwise convolutional filter to the first output to generate a second output based on global information from a spatial dimension and a channel dimension associated with the image, modifying the second output to the three-dimensional shape to generate a second set of features and combining the first set of features and the second set of features to generate an output set of features.