Adaptive Bit-Width Neural Network Processing with Nested Weights
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Solution Overview
Problem
Existing neural network models face inefficiencies in memory usage and processing speed due to fixed bit-width weights, which can be optimized for specific tasks but result in suboptimal performance when applied to varying processing demands.
Innovation Solution
A method and apparatus that adjust bit-widths of neural network weights based on processing characteristics, allowing for nested bit representations to optimize memory usage and performance by applying different bit-widths to different layers or networks, and using nested bit models to share memory space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed bit-width weights are used in neural networks, then memory usage is simplified and processing is faster, but adaptability to different processing demands deteriorates
Solution Approach 1:
The patent implements dynamic bit-width adjustment where the bit-width of weights is not fixed but can be changed based on processing characteristics. Different layers or networks can use different bit-widths (e.g., 8-bit, 4-bit, 2-bit) depending on their specific requirements for speed and accuracy, allowing the system to adapt dynamically to varying processing demands while maintaining the benefits of fixed-point arithmetic.
2Measurement precision
If high bit-precision weights are used, then processing accuracy is improved, but memory efficiency deteriorates
Solution Approach 1:
The patent applies different bit-widths to different layers or networks based on their specific requirements. Critical layers that require high accuracy use higher bit-widths (e.g., 8-bit), while less critical layers use lower bit-widths (e.g., 2-bit or 4-bit). This local differentiation optimizes the balance between accuracy and memory usage by allocating precision resources where they are most needed.
Solution Approach 2:
The patent changes the bit-width parameter of weights based on processing characteristics such as required accuracy, processing speed, and terminal performance. By adjusting this fundamental parameter, the system can optimize memory efficiency while maintaining adequate processing accuracy for each specific layer or task.
3Quantity of substance
If nested bit representation is implemented, then memory efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements nested bit representation where weights of different bit-widths are stored in a nested manner. For example, 2-bit weights are nested within 4-bit weights, which are nested within 8-bit weights. This allows the system to share memory space across different precision requirements, improving memory efficiency while the nesting structure itself manages the complexity.
Data Source
AI summary
A neural network processing method and apparatus based on nested bit representation is provided. The processing method includes obtaining first weights for a first layer of a source model of a first layer of a neural network, determining a bit-width for the first layer of the neural network, obtaining second weights for the first layer of the neural network by extracting at least one bit corresponding to the determined bit-width from each of the first weights for the first layer of a source model corresponding to the first layer of the neural network, and processing input data of the first layer of the neural network by executing the first layer of the neural network based on the obtained second weights.