Determining quantization scale factors for layers of a machine learning model
US12314863B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 12, 2021 |
| Grant date | May 27, 2025 |
| Priority date | — |
| Expiry date | Jan 21, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Approaches for determining quantization scale factors include generating a population of chromosomes. Each chromosome has multiple genes, and each gene specifies a scale factor associated with a layer of a machine learning model. The population of chromosomes are evaluated, and the evaluating includes, for each chromosome in the population, quantizing floating point weights and floating point values of a representative dataset using the scale factors of the chromosome to produce quantized weights and a quantized dataset in the memory arrangement, initiating processing of the quantized dataset using the quantized weights according to the machine learning model, and gauging a level of accuracy of results produced by the processing of the quantized dataset. Satisfaction of termination criteria is determined based the levels of accuracy associated with the chromosomes in the population. The population of chromosomes is evolved and the evaluating repeated in response to the termination criteria not being satisfied.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.