Dynamic Variable Quantization for Machine Learning Models
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Solution Overview
Problem
Existing machine learning models face challenges in balancing compression with accuracy, as conventional quantization techniques apply the same scheme to all models, leading to poor performance and varying effects based on input values, making it difficult to adapt to different hardware and latency requirements.
Innovation Solution
A dynamic variable quantization technique that generates multiple sets of quantized feature values with varying resolution levels, storing mappings in a lookup table to optimize memory footprint, inference speed, and computational overhead without compromising accuracy, allowing adaptation to different features, target values, and hardware platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If quantization is applied to reduce memory footprint and computational overhead, then resource consumption is reduced, but accuracy of the machine learning model deteriorates
Solution Approach 1:
The patent applies different quantization resolutions to different features based on their importance and sensitivity. Critical features that heavily impact accuracy are quantized at higher resolutions, while less sensitive features use lower resolutions. This local differentiation allows the system to reduce overall memory footprint while maintaining accuracy for the most important features.
Solution Approach 2:
The patent implements dynamic quantization where the quantization resolution is adjusted based on input data characteristics and model requirements. The system can adaptively select quantization levels during runtime, allowing optimization between memory usage and accuracy for different deployment scenarios and data distributions.
2Ease of manufacture
If a fixed quantization scheme is applied to all machine learning models, then implementation is simplified, but performance deteriorates due to inability to adapt to different models and input values
Solution Approach 1:
The patent changes the quantization parameters dynamically based on model type, feature importance, and input data characteristics. Instead of using a fixed quantization scheme, the system adjusts quantization resolution, step size, and other parameters to optimize performance for each specific model and input scenario while maintaining a unified implementation framework.
3Productivity
If heavy quantization is applied to reduce computational overhead, then processing speed improves, but accuracy deteriorates for certain input values and combinations
Solution Approach 1:
The patent applies higher quantization resolution to features and input combinations that are critical for maintaining accuracy, while using lower resolution for less sensitive dimensions. This localized approach allows the system to achieve fast processing for the majority of computations while preserving accuracy for critical calculations.
Solution Approach 2:
The patent applies quantization more heavily (lower resolution) to features where it has minimal impact on accuracy, and uses lighter quantization (higher resolution) only where necessary. This partial application of heavy quantization optimizes the trade-off between processing speed and accuracy by avoiding unnecessary computational overhead in less critical areas.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for quantizing a machine learning model. The technique includes generating a first set of quantized feature values based on a first set of feature values inputted into the machine learning model and a first set of quantization levels. The technique also includes determining that a first output generated by the machine learning model based on the first set of quantized feature values does not match a second output associated with the first set of feature values. The technique further includes generating a second set of quantized feature values based on the first set of feature values and a second set of quantization levels that is associated with a higher quantization resolution than the first set of quantization levels, and storing a first mapping of the second set of quantized feature values to the first output in a lookup table.