Abstract: Methods, apparatus, systems, and articles of manufacture to iteratively search for an artificial intelligence-based architecture are disclosed. An example apparatus includes an interface to access a first subgroup of architecture configurations from a search space; instructions; and processor circuitry to execute the instructions to: train first predictors based on the first subgroup; generate a first plurality of candidate architecture configurations using the trained first predictors; and generate a second subgroup of architecture configurations by selecting a number of the plurality of candidate architecture configurations; train second predictors based on the first subgroup and the second subgroup; and generate a second plurality of candidate architecture configurations using the trained second predictors.

Abstract: The present disclosure is related to machine learning model swap (MLMS) framework for that selects and interchanges machine learning (ML) models in an energy and communication efficient way while adapting the ML models to real time changes in system constraints. The MLMS framework includes an ML model search strategy that can flexibly adapt ML models for a wide variety of compute system and/or environmental changes. Energy and communication efficiency is achieved by using a similarity-based ML model selection process, which selects a replacement ML model that has the most overlap in pre-trained parameters from a currently deployed ML model to minimize memory write operation overhead. Other embodiments may be described and/or claimed.