Patent ID: 11928583
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A system, comprising:
a storage device to store a training corpus comprising training data, a parameters vector, and a plurality of edge-related metrics that are related to a plurality of resource constraints associated with an edge device; and
a processor to:
train an initial set of Deep Learning (DL) models comprising different topologies using the training data, wherein a topology of each of the initial set of DL models is determined based on the parameters vector;
wherein, in training of the initial set of DL models, the processor is to generate a set of estimated performance functions for each of the DL models in the initial set for each of the plurality of edge-related metrics, wherein each of the estimated performance functions comprises a set of values that are computed for each of the plurality of edge-related metrics that describe how a performance of each of the DL models changes throughout a range of parameters with respect to each of the plurality of edge-related metrics;
for each of the plurality of edge-related metrics, generate a plurality of objective functions based on the generated set of estimated performance functions;
train a plurality of final DL models selected from the initial set of models based on a multi objective optimization of the generated plurality of objective functions, wherein the plurality of final DL models are trained using new model parameters and a new estimated performance function is computed for each of the plurality of edge-related metrics with respect to each of the plurality of final DL models, wherein the new estimated performance functions are used to generate an updated plurality of objective functions and produce a new set of final DL models in response to detecting that a difference between the new estimated performance function of a final DL model from the plurality of final DL models, and the estimated performance functions of a DL model from the initial set of DL models, exceeds a threshold error criterion;
receive a user selection of a selected DL model from the new set of final DL models; and
deploy the selected DL model on the edge device.