Floating-Point Data Quantization for Bandwidth-Efficient Matrix Computing
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Solution Overview
Problem
The challenge of balancing performance and precision in large matrix operations within artificial intelligence models is exacerbated by the high computational demands and storage requirements of floating-point data, leading to bandwidth bottlenecks and precision loss when converting between different data types.
Innovation Solution
A data processing apparatus and method that utilizes a quantization unit to convert floating-point data into fixed-point data using maximum/minimum values, allowing for efficient matrix operations with reduced precision loss and maintaining chip performance by integrating quantization with data transfer units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If floating-point data is used for matrix operations, then calculation precision is improved, but storage requirements and bandwidth consumption increase
Solution Approach 1:
The patent changes the data representation parameters by converting floating-point data to fixed-point data with reduced precision. This allows the system to use less storage space and bandwidth while maintaining acceptable calculation precision for the specific matrix operations being performed. The quantization unit transforms the data format to achieve this parameter change.
Solution Approach 2:
The patent segments the data processing into different precision levels by using both fixed-point data (for most operations) and floating-point data (for critical operations). This segmentation allows the system to optimize the balance between precision and storage requirements by applying different data types to different parts of the computation graph.
2Measurement precision
If floating-point data is used for matrix operations, then calculation precision is improved, but bandwidth consumption increases causing bottlenecks
Solution Approach 1:
The patent changes the data representation parameters by converting floating-point data to fixed-point data with reduced precision. This allows the system to use less storage space and bandwidth while maintaining acceptable calculation precision for the specific matrix operations being performed. The quantization unit transforms the data format to achieve this parameter change.
3Quantity of substance
If quantization is applied to reduce storage needs, then storage requirements are reduced, but precision loss occurs
Solution Approach 1:
The patent segments the data processing into different precision levels by using both fixed-point data (for most operations) and floating-point data (for critical operations). This segmentation allows the system to optimize the balance between precision and storage requirements by applying different data types to different parts of the computation graph.
Solution Approach 2:
The patent implements a feedback mechanism where the system monitors calculation results and can dynamically adjust quantization parameters or switch between fixed-point and floating-point operations based on precision requirements. This feedback loop allows the system to minimize precision loss while maintaining storage efficiency.
4Quantity of substance
If data type conversion is performed, then storage efficiency is improved, but conversion time and complexity increase
Solution Approach 1:
The patent merges the quantization operation with the existing data transfer unit, combining multiple functions into a single integrated component. This reduces the overall system complexity by eliminating separate conversion hardware blocks and simplifying the data flow path while maintaining storage efficiency benefits.
Data Source
AI summary
A data processing apparatus relates to fields of artificial intelligence, chips, large models, and distributed computing platform technologies and includes: a quantization unit for quantizing floating-point data to be processed into first fixed-point data according to a first maximum/minimum value corresponding to the floating-point data to be processed; and quantizing second floating-point data into second fixed-point data according to a second maximum/minimum value corresponding to the floating-point data to be processed, the second floating-point data being obtained according to the floating-point data to be processed and first floating-point data obtained by dequantizing the first fixed-point data; and a first computing unit for obtaining a first calculation result according to fixed-point data to be processed and the first fixed-point data; obtaining a second calculation result according to the fixed-point data to be processed and the second fixed-point data; and obtaining a target calculation result according to first and second calculation results.