Quantization method for partial sums of convolution neural network based on computing-in-memory hardware and system thereof
US11423315B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 6, 2020 |
| Grant date | Aug 23, 2022 |
| Priority date | — |
| Expiry date | Apr 11, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A quantization method for a plurality of partial sums of a convolution neural network based on a computing-in-memory hardware includes a probability-based quantizing step and a margin-based quantizing step. The probability-based quantizing step includes a network training step, a quantization-level generating step, a partial-sum quantizing step, a first network retraining step and a first accuracy generating step. The margin-based quantizing step includes a quantization edge changing step, a second network retraining step and a second accuracy generating step. The quantization edge changing step includes changing a quantization edge of at least one of a plurality of quantization levels. The probability-based quantizing step is performed to generate a first accuracy value, and the margin-based quantizing step is performed to generate a second accuracy value. The second accuracy value is greater than the first accuracy value.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.