Row-by-row convolutional neural network mapping for analog artificial intelligence network training
US12050997B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 27, 2020 |
| Grant date | Jul 30, 2024 |
| Priority date | — |
| Expiry date | Jun 1, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG11C2213/79
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer implemented method for implementing a convolutional neural network (CNN) using a crosspoint array includes configuring the crosspoint array to implement a convolution layer by storing one or more weights in crosspoint devices of the array. The method further includes making multiple copies of the weights and training the CNN. Training the CNN includes mapping input data of the convolution layer to the crosspoint array in a row-by-row manner. Further the excitation is input in a row-by-row manner into the crosspoint array, thereby creating row-by-row forward output from the crosspoint array. Further, outputs from the crosspoint devices are stored to corresponding integrators. Errors in the outputs as compared to a desired output, from multiple rows are computed and back propagated in a row-by-row manner into the crosspoint array, the computed errors transmitted to a previous convolution layer.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.