Intermediate module neural architecture search

A method, comprising:

• identifying, by utilizing a neural network, a task to be completed;
• identifying, based on a characteristic of the task, candidate modules of the plurality of modules in a repository for substituting at least a portion of a block within an existing artificial intelligence model;
• training the candidate modules using intermediate features distillation on a distribution of data associated with a dataset;
• determining, based on the training, an accuracy rank for each of the candidate modules;
• executing candidate models including the candidate modules on a deep learning accelerator to determine a runtime execution rank for each of the candidate models;
• determining an optimal proposed model from the candidate models based on the accuracy rank and the runtime execution rank, wherein the optimal proposed model includes a portion of the existing artificial intelligence model and includes a substitution of the portion of the block with a candidate module of the plurality of candidate modules.

A device, comprising:

• a memory;
• and
• a processor;
• wherein the processor is configured to search, by utilizing a neural network, for a plurality of modules in a plurality of repositories;
• wherein the processor is configured to analyze first characteristics of the plurality of modules for inclusion in a search space;
• selecting a set of candidate modules of the plurality of modules in the plurality of repositories based on a matching of the first characteristics with second characteristics associated with a task to be completed;
• wherein the processor is configured to determine an optimal proposed model including at least one candidate module from the set of candidate modules based on an accuracy rank and a runtime execution rank of the candidate modules in the set of candidate modules;
• and
• wherein the processor is configured to execute the task using the optimal proposed model.