Efficient data encoding for deep neural network training
US11715002B2 · kind B2 · utility
2Cited by
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19Claims
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Key dates
| Filing date | Jun 29, 2018 |
| Grant date | Aug 1, 2023 |
| Priority date | — |
| Expiry date | Dec 16, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F9/5016
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Functions are added to a deep neural network (“DNN”) computation graph for encoding data structures during a forward training pass of the DNN and decoding previously-encoded data structures during a backward training pass of the DNN. The functions added to the DNN computation graph can be selected based upon on the specific layer pairs specified in the DNN computation graph. Once a modified DNN computation graph has been generated, the DNN can be trained using the modified DNN computation graph. The functions added to the modified DNN computation graph can reduce the utilization of memory during training of the DNN.
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