Dynamic Asymmetric Truncation Thresholds for AI Data Quantization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As artificial intelligence algorithms become more complex and large, the processing of increasing amounts of data leads to high calculation and time overhead, resulting in low processing efficiency.
Innovation Solution
A method and apparatus for data processing that involves obtaining data for a machine learning model, quantizing it using pairs of truncation thresholds, and selecting suitable thresholds based on the difference between the mean absolute value of the quantized data and the original data to minimize precision loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data quantization is performed using traditional methods, then processing efficiency is improved, but precision loss increases
Solution Approach 1:
The patent changes the parameter of truncation threshold from fixed/symmetric to dynamic/asymmetric. By making the positive and negative truncation thresholds asymmetric based on the data distribution characteristics, the quantization process adapts to the actual data, reducing precision loss while maintaining processing efficiency. The asymmetric thresholds are determined by analyzing the mean absolute values of positive and negative data separately.
Solution Approach 2:
The patent introduces dynamic adjustment of truncation thresholds based on data characteristics. Instead of using static symmetric thresholds, the system dynamically determines asymmetric thresholds by calculating the mean absolute values of positive and negative data portions, allowing the quantization process to adapt to different data distributions and minimize precision loss.
2Reliability
If more data is processed to improve model accuracy, then model performance is improved, but calculation overhead increases
Solution Approach 1:
The patent extracts and processes only the essential statistical characteristics (mean absolute values) of the data to determine truncation thresholds, rather than performing complex calculations on all data points. This extraction approach reduces calculation overhead while still achieving effective quantization that maintains model accuracy.
3Ease of manufacture
If symmetric truncation thresholds are used, then implementation simplicity is maintained, but precision loss increases
Solution Approach 1:
The patent explicitly applies asymmetry by using different truncation thresholds for positive and negative data portions. The positive truncation threshold and negative truncation threshold are determined separately based on the mean absolute values of positive and negative data, respectively. This asymmetric approach better matches the actual data distribution and reduces precision loss compared to symmetric thresholds.
Data Source
AI summary
Embodiments of the present disclosure relate to a method and an apparatus for processing data, and related products. The embodiments of the present disclosure relate to a board card, which includes a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus respectively. The storage component is used to store data, the interface apparatus is used to realize data transmission between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card may be used to perform artificial intelligence computations.