Hearing Aid Weight Encoding for Low-Power Neural Processing
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Solution Overview
Problem
Hearing devices implementing machine learning and deep neural networks (DNNs) are computationally costly and can negatively impact efficiency.
Innovation Solution
A hearing device with a processor that retrieves weights from a weight data structure using an index parameter, where the weight representation is compactly encoded with N-bits and an index parameter of J-bits, enabling efficient processing and memory usage through 4-bit non-uniform quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep neural networks are implemented in hearing devices, then processing capability and intelligence are improved, but computational cost and energy consumption increase
Solution Approach 1:
The patent applies parameter changes by quantizing neural network weights from high precision (e.g., 32-bit floating point) to low precision (e.g., 4-bit integers). This fundamental parameter change in weight representation enables efficient hardware implementation using integer arithmetic units, significantly reducing energy consumption while preserving sufficient processing capability for hearing device applications
Solution Approach 2:
The patent implements local quality by applying different precision levels to different parts of the neural network system. Specifically, weights are stored in low precision (4-bit) format for efficient processing, while the input data and intermediate computations maintain higher precision to preserve accuracy. This localized precision strategy optimizes the trade-off between energy consumption and processing capability
2Extent of automation
If deep neural networks are implemented in hearing devices, then processing capability is improved, but device complexity increases
Solution Approach 1:
By changing the weight representation parameter from high-precision floating point to low-precision integer format, the patent simplifies the computational operations required. This enables the use of simpler integer arithmetic units instead of complex floating-point units, thereby reducing device complexity while maintaining adequate processing capability
Solution Approach 2:
The patent extracts and separates the weight storage and retrieval functions from the main processing path. By using dedicated weight memory structures and efficient weight retrieval mechanisms, the system reduces the complexity of the main processing pipeline, allowing complex neural network functionality to be achieved without proportionally increasing overall device complexity
3Reliability
If full precision weights are stored, then network performance is maintained, but memory requirements increase
Solution Approach 1:
The patent applies parameter changes by representing neural network weights in low precision (e.g., 4-bit) format instead of full precision (e.g., 32-bit). This parameter change reduces memory requirements by a factor of 8 while the patent demonstrates that network performance is maintained at acceptable levels for hearing device applications through careful quantization strategies
Solution Approach 2:
The patent implements local quality by applying non-uniform quantization where different weight ranges are represented with different precision levels. Frequently used weight values are represented with higher precision while less critical weights use lower precision, thereby optimizing the balance between memory requirements and network performance
4Quantity of substance
If low precision quantization is applied, then memory efficiency is improved, but network performance deteriorates
Solution Approach 1:
The patent applies local quality through non-uniform quantization where different quantization schemes are used for different weight ranges. Critical weights that significantly impact network performance are represented with higher precision, while less critical weights use lower precision representation. This localized precision strategy maintains network performance while improving memory efficiency
Solution Approach 2:
The patent implements dynamics by using adaptive quantization where the precision allocation can be dynamically adjusted based on the specific neural network layer and its importance. This dynamic approach allows the system to allocate memory resources efficiently while maintaining network performance by preserving higher precision for more critical computations
Data Source
AI summary
A hearing device is disclosed. The hearing device comprises a set of input transducers for provision of transducer input data, the set of input transducers comprising a first input transducer for provision of a first transducer input signal as part of the transducer input data. The hearing device comprises a processor for processing transducer input data and providing an electrical output signal based on the transducer input data. The hearing device comprises a receiver for converting the electrical output signal to an audio output signal. The hearing device comprises a memory having stored thereon a weight representation indicative of a weight of a plurality of weights of a neural network based on the transducer input data.