Device placement optimization with reinforcement learning
US11803747B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 20, 2020 |
| Grant date | Oct 31, 2023 |
| Priority date | — |
| Expiry date | Jun 26, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/04
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.