Associative LSTM Layer Parallel State Replication
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Solution Overview
Problem
Recurrent neural networks with long short-term memory (LSTM) architectures face challenges in maintaining reliable internal state values and detecting long-term dependencies due to noise and potential internal failures, particularly when tracking multiple elements in input data.
Innovation Solution
Implementing an associative LSTM layer that maintains multiple copies of internal state values, updates them using layer inputs and previous outputs, and generates outputs by combining modified internal state copies, thereby reducing noise and improving resilience through indexing and averaging mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple copies of internal state are maintained and updated, then reliability and noise reduction are improved, but device complexity increases
Solution Approach 1:
The patent maintains N copies of the internal state (c_t^1, c_t^2, ..., c_t^N) in parallel, where each copy is updated independently using the same input and previous state. This copying approach reduces noise through averaging while providing redundancy to mitigate internal failures, directly resolving the contradiction between reliability and complexity by using simple replication rather than complex error-correcting mechanisms
Solution Approach 2:
The patent combines N modified copies of internal state through averaging to generate the final output: c_t = (1/N) * Σ(c_t^i). This merging operation consolidates the information from multiple copies into a single reliable state, achieving noise reduction and reliability improvement without proportionally increasing the overall system complexity
2Measurement precision
If multiple copies of internal state are maintained and updated, then detection precision of long-term dependencies is improved, but loss of time for computation increases
Solution Approach 1:
The patent segments the internal state into N independent copies that can be updated in parallel. Each copy processes the same input independently, allowing the computation to be divided into separate tasks that can execute simultaneously, thereby improving detection precision through multiple perspectives while reducing the actual time penalty through parallelization
Solution Approach 2:
The patent uses N copies where N is an integer greater than one, employing a degree of redundancy that exceeds the minimum single copy. This excessive action of maintaining multiple copies improves the detection precision of long-term dependencies through averaging and collision mitigation, while the parallel computation structure ensures the time cost remains manageable
3Reliability
If permutation matrices are applied to transformed input keys, then collision mitigation is improved, but device complexity increases
Solution Approach 1:
The patent applies different permutation matrices P_1, P_2, ..., P_N to the transformed input keys for each copy of the internal state. This asymmetric transformation ensures that each copy processes the input key differently, reducing the probability of collisions where different inputs produce the same key. The asymmetry introduced by unique permutation matrices for each copy mitigates collisions while adding only moderate complexity through standard linear algebra operations
Data Source
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AI summary
Systems, methods, and apparatus, including computer programs encoded on a computer storage medium, related to associative long short-term memory (LSTM) neural network layers configured to maintain N copies of an internal state for the associative LSTM layer, N being an integer greater than one. In one aspect, a system includes a recurrent neural network including an associative LSTM layer, wherein the associative LSTM layer is configured to, for each time step, receive a layer input, update each of the N copies of the internal state using the layer input for the time step and a layer output generated by the associative LSTM layer for a preceding time step, and generate a layer output for the time step using the N updated copies of the internal state.