Dilated Convolution Attention Network for Linear Long-Sequence Modeling
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Solution Overview
Problem
Existing sequence modeling techniques, such as transformers, suffer from quadratic computational complexity, limiting their efficiency on longer sequences, while alternative approaches like state-space models and moving-average equipped gated attention (MEGA) either remain computationally expensive or inaccurate on long-range classification tasks.
Innovation Solution
A dilated convolution and attention-based neural network architecture is introduced, utilizing dilated convolutions to capture long-range dependencies with linear complexity, combined with chunked self-attention to maintain efficiency and accuracy on long sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformer models are used for sequence modeling, then accuracy on sequence tasks is improved, but computational complexity increases quadratically with sequence length
Solution Approach 1:
The patent divides the sequence into chunks of fixed size and applies attention mechanisms only within each chunk rather than across the entire sequence. This segmentation reduces the computational complexity from quadratic O(N²) to linear O(N) while maintaining accuracy by processing local relationships efficiently and combining results across chunks.
Solution Approach 2:
The patent changes the parameter of attention scope from global (all sequence elements) to local (fixed-size chunks). By modifying this key parameter, the model achieves linear complexity while preserving the ability to capture important sequence relationships through the combination of dilated convolutions and chunked attention.
2Device complexity
If state-space models are used to reduce computational complexity, then linear complexity is achieved, but accuracy on long-range classification tasks deteriorates
Solution Approach 1:
The patent merges dilated convolutional networks with chunked self-attention mechanisms into a hybrid architecture. The dilated convolutions provide linear complexity and capture long-range dependencies, while the chunked attention blocks reinforce local relationships and classification accuracy. This combination resolves the trade-off by integrating the strengths of both components.
Solution Approach 2:
The patent creates a composite neural network architecture combining different processing mechanisms (dilated convolutions and attention blocks) to achieve both linear complexity and high accuracy. This composite structure leverages the complementary properties of each component to overcome the limitations of individual approaches.
3Productivity
If moving-average equipped gated attention (MEGA) is used, then computational efficiency is improved, but accuracy on long sequences deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the sequence into fixed-size chunks and applying attention within each chunk. This approach maintains computational efficiency similar to MEGA while improving accuracy by preserving more information about long-range dependencies through the dilated convolution structure that operates on the chunked sequences.
Data Source
AI summary
Examples described herein provide a computer-implemented method that includes receiving, at a dilated convolution and attention-based neural network, vector embeddings corresponding to sequence elements of sequential data. The dilated convolution and attention-based neural network includes a dilated convolutional neural network, a plurality of block-local attention blocks, and a feed-forward neural network. The method further includes generating, using the dilated convolution and attention-based neural network, a sequence of vector embeddings based at least in part on the sequential data.