Four-Bit Quantization Format for Accurate ML Inference
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Solution Overview
Problem
Existing machine learning systems face inefficiencies in representing numerical values using binary data formats, particularly in quantized operations, leading to increased computational costs and reduced accuracy.
Innovation Solution
Implementing a four-bit binary data format that correlates sixteen symmetric numerical values, with non-uniform step sizes, to represent numerical values, enabling efficient quantization and processing of machine-learned models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard binary data formats (e.g., 64-bit floating-point) are used to represent numerical values in machine learning models, then measurement precision and reliability are maintained, but device complexity and computational costs increase
Solution Approach 1:
The patent segments the numerical value representation into discrete quantization levels (e.g., 16 levels for 4-bit format), dividing the continuous range into manageable discrete steps. This segmentation enables simplified hardware implementation while maintaining acceptable precision for machine learning operations.
Solution Approach 2:
The patent changes the parameter of binary data format from standard high-precision formats (64-bit floating-point) to compressed low-precision formats (4-bit with 16 quantization levels). This parameter change reduces computational complexity and memory requirements while preserving sufficient accuracy through optimized quantization schemes.
2Measurement precision
If high-precision binary data formats are used, then measurement precision is maintained, but loss of substance (memory usage) and computational costs increase
Solution Approach 1:
The patent divides the parameter space into discrete quantization bins (e.g., 16 bins for 4-bit representation), allowing model parameters to be stored in compressed form. This segmentation reduces memory bandwidth consumption during model loading and inference while maintaining sufficient precision through strategic bin placement.
Solution Approach 2:
The patent uses lookup tables that store pre-computed quantization mappings, creating a compressed copy of the parameter representation. Instead of storing full-precision parameters, the system stores index values that reference pre-computed quantized values, significantly reducing memory usage.
3Ease of operation
If uniform quantization steps are used in binary data formats, then ease of manufacture and operation are improved, but measurement precision deteriorates
Solution Approach 1:
The patent employs asymmetric quantization where the step sizes between adjacent quantization levels are not uniform. Larger steps are used for less critical value ranges while smaller steps are used for more critical ranges, optimizing the balance between precision and operational simplicity.
Solution Approach 2:
The patent applies different quantization characteristics to different regions of the numerical range. Critical regions (e.g., near zero or in high-frequency ranges) receive finer quantization resolution, while less critical regions use coarser resolution, optimizing overall precision without uniform complexity.
Data Source
AI summary
A computing system can obtain a machine-learned model comprising one or more parameters having a four-bit binary format. The four-bit binary format can correlate a plurality of sixteen respective binary values to a plurality of sixteen corresponding numerical values represented by the binary values. The sixteen numerical values can be symmetric about a median. A plurality of step sizes between the sixteen numerical values can be non-uniform. The computing system can obtain one or more input values for one or more layers of the machine-learned model. The computing system can process, based at least in part on the one or more parameters having the four-bit binary format, the one or more input values to generate one or more output values.