Hierarchical Attention Memory for Neural Network Efficiency
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Solution Overview
Problem
Conventional attention-based neural networks face inefficiencies in computational resources and struggle with long-range sequential tasks, especially in reinforcement learning, due to ineffective attention mechanisms that fail to efficiently recall temporally-structured events.
Innovation Solution
The implementation of a hierarchical attention mechanism that generates and maintains memory summary keys to identify relevant partitions, allowing for efficient attention within those partitions, thereby reducing computational overhead and improving task performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional attention mechanisms are used to process sequential inputs, then the network can capture temporal dependencies, but computational resource consumption increases and efficiency decreases
Solution Approach 1:
The patent segments the sequential processing into discrete memory blocks that are stored and retrieved efficiently. Instead of processing entire sequences through heavy attention mechanisms, the system divides memory into manageable blocks that can be selectively accessed, reducing computational overhead while preserving temporal dependency information.
Solution Approach 2:
The patent introduces a hierarchical dimension to memory organization, creating memory summaries at multiple levels (block level and partition level). This dimensional transformation allows the system to navigate temporal dependencies through hierarchical indexing rather than brute-force attention, improving efficiency while maintaining reliability.
2Loss of information
If attention mechanisms process all memory positions, then complete information is accessed, but computational overhead increases significantly
Solution Approach 1:
The patent performs preliminary organization of memory into structured blocks with summaries created in advance. Memory summaries are pre-computed for each block and partition, allowing the system to quickly identify relevant information without scanning entire sequences during processing, thus reducing computational overhead while maintaining information completeness.
Solution Approach 2:
The system implements selective attention by processing only relevant memory blocks and partitions identified through hierarchical indexing. Instead of attending to all memory positions uniformly, the network applies attention selectively to subsets of memory that are most relevant to the current task, reducing computational overhead while preserving necessary information.
3Ease of operation
If memory is organized without hierarchical structure, then access is simpler, but recall of temporally-structured events becomes inefficient
Solution Approach 1:
The patent implements a nested hierarchical structure where memory partitions are contained within blocks, and blocks are organized in sequences. This nesting creates a multi-level indexing system that maintains simplicity at each level while enabling efficient temporal recall through hierarchical navigation, combining ease of access with fast recall of temporally-structured events.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing a machine learning task on a network input to generate a network output. One of the systems includes an attention neural network comprising one or more hierarchical attention blocks, each hierarchical attention block configured to: receive an input sequence for the hierarchical attention block; maintain a 5 plurality of memory summary keys, each memory summary key corresponding to a respective one of a plurality of partitions of a sequence of memory block inputs; determine a proper subset of the plurality of memory summary keys; and generate an attended input sequence for the hierarchical attention block including applying an attention mechanism over the respective memory block inputs at the memory positions within the partitions of 10 the sequence of memory block inputs that correspond to the proper subset of the plurality of memory summary keys.