Dynamic Convolution Kernel Adaptation for Mobile AI
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Solution Overview
Problem
Existing dynamic convolution methods for AI algorithms in resource-constrained environments, such as smartphones, face challenges with high computational overhead and quantization errors due to real-number kernel adaptation weights, which hinder efficient operation and accuracy.
Innovation Solution
The method employs kernel adaptation weights represented by discrete values, using Gumbel softmax sampling to determine weights in a category set, allowing for the aggregation of multiple convolution kernels into a unified kernel without full precision MAC operations, thereby reducing computational burden and quantization errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-number kernel adaptation weights are used in dynamic convolution, then representation capacity and network accuracy are improved, but computational overhead and quantization errors increase
Solution Approach 1:
The patent changes the parameter type of kernel adaptation weights from real numbers to discrete values. This parameter transformation reduces computational complexity and quantization errors while maintaining adequate representation capacity. The discrete values are selected from a predefined set, converting continuous parameter optimization into discrete selection, which is more suitable for resource-constrained devices.
Solution Approach 2:
The patent segments the continuous space of kernel adaptation weights into discrete categories or levels. Instead of using continuous real-number weights, the system divides the weight space into discrete bins or levels, allowing the model to select from predefined discrete values. This segmentation reduces the computational burden while preserving the essential adaptive functionality.
2Measurement precision
If real-number kernel adaptation weights are used in dynamic convolution, then representation capacity and network accuracy are improved, but quantization errors increase
Solution Approach 1:
The patent transforms the parameter representation from continuous real numbers to discrete values, which are less susceptible to quantization errors. By using discrete values from a predefined set, the system avoids the precision loss inherent in representing and processing continuous real numbers in finite-precision arithmetic, thereby reducing quantization errors while maintaining network accuracy.
Solution Approach 2:
The patent employs discrete kernel adaptation weights that can be pre-computed and stored, replacing the need for continuous real-number computations during inference. These discrete weights act as simplified, pre-prepared objects that are computationally cheaper and more stable, trading off some continuous adaptability for robustness against quantization errors.
3Adaptability or versatility
If multiple convolution kernels are aggregated into a unified kernel using full precision MAC operations, then representation capacity is improved, but operation overhead increases
Solution Approach 1:
The patent changes the aggregation operation from full precision multiply-accumulate (MAC) operations to discrete value selection and combination. Instead of performing computationally intensive real-number multiplications and additions, the system selects discrete kernel adaptation weights from a predefined set and combines kernels using simpler operations, significantly reducing computational overhead while maintaining representation capacity.
Solution Approach 2:
The patent introduces dynamic selection of discrete kernel adaptation weights based on input characteristics, allowing the unified kernel to adapt to different input scenarios. This dynamic approach uses discrete weights that are selected or adjusted based on input data properties, providing adaptability without the full computational burden of continuous real-number optimization during inference.
Data Source
AI summary
A method with dynamic convolution includes: determining kernel adaptation weights corresponding to weight matrices in a category set represented by a plurality of predetermined discrete values; determining a unified kernel based on the weight matrices and the kernel adaptation weights corresponding to the weight matrices; and performing a convolution operation based on the unified kernel.