Hearing Aid Parallel Neural Networks With Staggered State Resets
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Solution Overview
Problem
Stateful neural networks, such as recurrent neural networks, suffer from degradation over time due to state drifting, leading to performance issues, and resetting these states can introduce immediate performance degradation.
Innovation Solution
Implementing multiple neural networks in parallel with staggered reset times and dynamic weighting schemes to ensure at least one network has optimal state information, combining outputs to maintain performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If states of a neural network are reset to prevent degradation over time, then long-term performance is maintained, but immediate performance degrades due to loss of state information
Solution Approach 1:
The system divides the neural network into multiple parallel instances (first neural network and second neural network). Each instance can be independently reset without affecting the other, allowing the system to segment the risk of performance degradation and ensure continuous optimal operation.
Solution Approach 2:
The system performs preliminary actions by resetting one neural network while the other is still operational and maintaining optimal performance. This allows the first network to be prepared and reset in advance before the second network needs resetting, ensuring continuous coverage.
Solution Approach 3:
The system implements periodic resetting of neural network states in a staggered manner. Instead of resetting both networks simultaneously or continuously, it alternates resetting between the first and second networks at different time intervals, ensuring that at least one network is always in an optimal state.
2Reliability
If multiple neural networks operate in parallel to maintain performance, then reliability improves, but device complexity increases
Solution Approach 1:
The system merges multiple neural networks into a unified architecture where they share common components such as input layers, processing logic, and output interfaces. This allows parallel operation while reducing overall complexity through component sharing and standardized integration.
Solution Approach 2:
The parallel neural networks are designed with universal architectures that can perform the same functions interchangeably. This multi-functionality allows the system to switch between networks seamlessly and maintain performance while using standardized, reusable components that reduce complexity.
Data Source
AI summary
An apparatus (e.g., an ear-worn device such as a hearing aid) includes neural network circuitry and control circuitry. The neural network circuitry is configured to implement a neural network system comprising at least a first neural network and a second neural network operating in parallel. The control circuitry is configured to control the neural network system to receive a first input signal, process the first input signal using the first neural network to produce a first output and using the second neural network to produce a second output, combine the first output and the second output, reset one or more states of the first neural network, and reset one or more states of the second neural network at a different time than when the one or more states of the first neural network are reset.