18727800. Partitioned Inference And Training Of Large Models (Google LLC)
Partitioned Inference And Training Of Large Models
Organization Name
Inventor(s)
Matthew Sharifi of Kilchberg CH
David Petrou of Brooklyn NY US
Blaise Aguera Y Arcas of Seattle WA US
Partitioned Inference And Training Of Large Models
This abstract first appeared for US patent application 18727800 titled 'Partitioned Inference And Training Of Large Models
Original Abstract Submitted
Systems and methods for partitioning a large model that has been configured to use a model-synthesis approach in which multiple basis models are combined to generate a final output. The present technology provides systems and methods for identifying a device-specific or subject-specific subset of those basis models to be used on a given device, such that it need not store the weight matrices for the entire set of basis models, and may perform inference using only the weight matrices of the identified subset of basis models. In some examples, the subset of basis models used by a given device may be updated based on actual usage and feedback. Likewise, in some examples, the model may be trained in a federated setting in which multiple devices each utilize different subsets of the basis models, and share training signals with a full copy of the model.
