Data processing performance enhancement for neural networks using a virtualized data iterator
US10795836B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 1, 2017 |
| Grant date | Oct 6, 2020 |
| Priority date | — |
| Expiry date | Mar 18, 2039 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02D30/50
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. Using virtualized hardware iterators, data for processing by the NN/DNN can be traversed and configured to optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, an iterator controller can generate instructions for execution by the NN/DNN representative of one more desired iterator operation types and to perform one or more iterator operations. Data can be iterated according to a selected iterator operation and communicated to one or more neuron processors of the NN/DD for processing and output to a destination memory. The iterator operations can be applied to various volumes of data (e.g., blobs) in parallel or multiple slices of the same volume.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.