System and method for providing a task- and hardware-architecture-specific machine learning model

A system, comprising:

• one or more processors;
• and
• one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors to perform steps of a method for providing a task-specific and hardware-architecture-specific machine learning model, the method comprising the following steps:
• providing a trained superposition model, wherein the trained superposition model includes a superposition of a set of machine learning models, wherein individual machine learning models are extractable from the trained superposition model;
• receiving a characterization of a target hardware architecture;
• finetuning the trained superposition model for an application task in a hardware-architecture-agnostic way, wherein the superposition model has been trained using a general dataset and wherein the finetuning includes using a labelled dataset which is specific for the application task;
• selecting a machine learning model from the finetuned superposition model, wherein the selecting of the machine learning model includes performing, for the target hardware architecture, a search using a first function describing a first performance of a candidate machine learning model for the application task and a second function describing a second performance of the candidate machine learning model when executed on the target hardware architecture;
• providing the selected machine learning model as output for deployment on the target hardware architecture.

A non-transitory computer-readable medium on which are stored data representing instructions, which, when executed by a processor system, cause the processor system to perform a for providing a task-specific and hardware-architecture-specific machine learning model, the method comprising the following steps:

• providing a trained superposition model, wherein the trained superposition model includes a superposition of a set of machine learning models, wherein individual machine learning models are extractable from the trained superposition model;
• receiving a characterization of a target hardware architecture;
• finetuning the trained superposition model for an application task in a hardware-architecture-agnostic way, wherein the superposition model has been trained using a general dataset and wherein the finetuning includes using a labelled dataset which is specific for the application task;
• selecting a machine learning model from the finetuned superposition model, wherein the selecting of the machine learning model includes performing, for the target hardware architecture, a search using a first function describing a first performance of a candidate machine learning model for the application task and a second function describing a second performance of the candidate machine learning model when executed on the target hardware architecture;
• providing the selected machine learning model as output for deployment on the target hardware architecture.