Sub-Quadratic Iterator Modules for Machine Learning Sequences
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Solution Overview
Problem
Existing machine learning models face challenges in efficiently transferring information across long sequences due to quadratic computational complexity, which limits their performance and scalability in applications like large language models and genomics.
Innovation Solution
The implementation of sub-quadratic iterator modules with iterative application, coupled with worker embeddings that allow for amortized compute loads independent of sequence length, enables efficient information transfer while maintaining contextual fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional machine learning models use standard attention mechanisms to transfer information across sequences, then information transfer capability is improved, but computational complexity increases quadratically with sequence length
Solution Approach 1:
The patent divides the sequence processing into multiple iterations of a unified architecture, where each iteration processes a portion of the information transfer task. This segmentation allows the model to achieve comprehensive information transfer across long sequences while maintaining sub-quadratic computational complexity per iteration, resolving the contradiction between information transfer capability and computational complexity.
Solution Approach 2:
The patent employs dynamic iteration where the number of iterations and processing depth can be adjusted based on sequence length and task requirements. This dynamic approach allows the system to adapt computational resources to the actual information transfer needs, achieving high information transfer capability without always incurring quadratic complexity costs.
2Manufacturing precision
If machine learning models increase processing depth to improve output quality, then output quality is improved, but computational time increases
Solution Approach 1:
The patent uses periodic iteration where the processing is divided into multiple cycles, each cycle performing a subset of the total processing depth. This periodic action allows the model to progressively improve output quality across iterations while controlling the computational time per iteration to be sub-quadratic, resolving the contradiction between output quality and computational time.
Solution Approach 2:
The system dynamically adjusts the number of iterations and processing depth based on task requirements and resource constraints, allowing flexible control over the trade-off between output quality and computational time.
3Manufacturing precision
If machine learning models increase processing depth to improve output quality, then output quality is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex deep processing task into multiple simpler iterative steps with unified architecture. Each iteration uses a standardized sub-quadratic processing module, which simplifies the overall architectural complexity compared to a single monolithic deep model, while still achieving high output quality through cumulative processing across iterations.
Data Source
AI summary
Technology that includes receiving one or more inputs representative of an input sequence, and passing the one or more inputs sequentially through a plurality of modules of a machine learning model. Each module of the plurality of modules includes weights used for processing the one or more inputs to generate one or more outputs, and at least one of the plurality of modules is an iterator module that feeds back intermediate outputs of the iterator module as inputs to itself one or more times before passing an iterated output to a subsequent module of the plurality of modules. The iterator module has a sub-quadratic computational complexity in relation to a length of the input sequence. The technology also includes outputting the one or more outputs generated by a final module of the plurality of modules.