Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to optimize resources in edge networks. An example apparatus includes agent managing circuitry to invoke an exploration agent to identify platform resource devices, select a first one of the identified platform resource devices, and generate first optimization metrics for the workload corresponding to the first one of the identified platform resource devices, the first optimization metrics corresponding to a first path. The example agent is to further select a second one of the identified platform resource devices, generate second optimization metrics for the workload corresponding to the second one of the identified platform resource devices, the second optimization metrics corresponding to a second path.

Abstract: Methods, apparatus, systems, and articles of manufacture for dynamic XPU hardware-aware deep learning model management are disclosed. An example method includes extracting a plurality of models from a dataset, respective ones of the plurality of models optimized for a selected quality of service (QoS) objective of a plurality of QoS objectives, identifying a plurality of feature differences between respective ones of the plurality of models, and identifying a plurality of feature similarities between respective ones of the plurality of models.

Abstract: Methods, apparatus, systems, and articles of manufacture for data enhanced automated model generation are disclosed. Example instructions, when executed, cause at least one processor to access a request to generate a machine learning model to perform a selected task, generate task knowledge based on a previously generated machine learning model, create a search space based on the task knowledge, and generate a machine learning model using neural architecture search, the neural architecture search beginning based on the search space.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for composable machine learning compute nodes. An example apparatus includes interface circuitry to receive a workload, instructions in the apparatus, and processor circuitry to at least one of execute or instantiate the instructions to generate a first configuration of one or more machine-learning models based on a workload, generate a second configuration of hardware, determine an evaluation parameter based on an execution of the workload, the execution of the workload based on the first configuration and the second configuration, and, in response to the evaluation parameter satisfying a threshold, execute the one or more machine-learning models in the first configuration on the hardware in the second configuration, the one or more machine-learning models and the hardware to execute the workload.

Abstract: Methods, apparatus, systems and articles of manufacture for low precision training of a machine learning model are disclosed. An example apparatus includes a low precision converter to calculate an average magnitude of weighting values included in a tensor, the weighting values represented in a high precision format, the low precision converter to calculate a maximal magnitude of the weighting values included in the tensor, determine a squeeze factor and a shift factor based on the average magnitude and the maximal magnitude, and convert the weighting values from the high precision format into a low precision format based on the squeeze factor and the shift factor. A model parameter memory is to store the tensor as part of a machine learning model, the tensor including the weighting values represented in the low precision format, the shift factor, and squeeze factor. A model executor is to execute the machine learning model.