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

Claim 17:
18. A computer program product for deploying Deep Learning (DL) models on edge devices comprising a computer readable storage medium having program instructions embodied therewith, and wherein the program instructions are executable by a processor to cause the processor to:
train an initial set of DL models comprising different topologies using training data, wherein a topology of each of the initial set of DL models is determined based on a parameters vector that specifies a number of layers and a number of nodes per layer for each model in the initial set of DL models;
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 a plurality of edge-related metrics comprising an inference time, a model size, and a test accuracy, 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;
generate a plurality of objective functions for each of the plurality of edge-related metrics 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.