Attention Matrix Computation via Key Reordering
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Solution Overview
Problem
Transformer models for natural language processing consume significant energy during attention mechanism computations due to high energy expenditure in analog accelerators, particularly in microring resonator-based systems, which is exacerbated by the order of multiplications in dot product operations.
Innovation Solution
Reordering the columns of keys in descending order to minimize state changes in microring resonators during dot product operations, thereby reducing energy consumption in analog accelerators used for computing attention matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If dot product operations are performed using analog accelerators with microring resonators, then computation speed is improved, but energy consumption increases
Solution Approach 1:
The patent applies preliminary action by reordering the columns of keys in descending order before performing dot product operations. This reordering is done in advance to minimize state changes in microring resonators during computation, thereby reducing energy consumption while maintaining computation speed. The reordering step prepares the data in an optimal sequence that prevents excessive switching activity in the analog accelerator.
Solution Approach 2:
The patent changes the parameter of key column ordering from arbitrary or default sequence to descending order based on some metric (e.g., magnitude or importance). This parameter change in the data organization directly impacts the operational characteristics of microring resonators, reducing the frequency and magnitude of state transitions, and consequently lowering energy consumption during the analog computation process.
2Ease of manufacture
If standard ordering of key columns is used in dot product operations, then implementation simplicity is maintained, but energy consumption increases due to excessive state changes
Solution Approach 1:
The patent introduces a preliminary reordering step that sorts key columns in descending order before processing. While this adds a preprocessing step, it significantly reduces energy consumption during the main computation by minimizing state changes in microring resonators. The benefit in energy efficiency far outweighs the minor additional complexity of the sorting operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces energy expenditure by minimizing the differences between adjacent key components, leading to lower energy consumption and more efficient computations in attention matrix calculations.
Implementation Method 1
analog accelerators, particularly in microring resonator-based systems
Data Source
AI summary
An apparatus to compute an attention matrix implementing an attention mechanism in artificial neural networks, having: memory to store key value pairs; a reorder buffer to provide a reordered list of keys from the key value pairs; an analog dot product accelerator configured to compute dot products of key elements of keys from the reordered list of keys with respective query elements of a query row of a query matrix; a processing device configured to generate, based on results of the dot products, a row of attention scores corresponding to the query row of the query matrix for the reordered list of keys; and a further accelerator configured to compute dot products of segments of the attention scores with value elements of respective segments of values from a list of values from the key value pairs to generate an attention matrix.