Histogram-based per-layer data format selection for hardware implementation of deep neural network
US11593626B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 5, 2018 |
| Grant date | Feb 28, 2023 |
| Priority date | — |
| Expiry date | Sep 11, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/045
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A histogram-based method of selecting a fixed point number format for representing a set of values input to, or output from, a layer of a Deep Neural Network (DNN). The method comprises obtaining a histogram that represents an expected distribution of the set of values of the layer, each bin of the histogram is associated with a frequency value and a representative value in a floating point number format; quantising the representative values according to each of a plurality of potential fixed point number formats; estimating, for each of the plurality of potential fixed point number formats, the total quantisation error based on the frequency values of the histogram and a distance value for each bin that is based on the quantisation of the representative value for that bin; and selecting the fixed point number format associated with the smallest estimated total quantisation error as the optimum fixed point number format for representing the set of values of the layer.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.