Systems and Methods for Providing a Machine-Learned Model with Adjustable Computational Demand

A computing device, comprising:

• at least one processor;
• a machine-learned model comprising a plurality of blocks and one or more residual connections between two or more of the plurality of blocks, wherein the machine-learned model is configured to receive a model input and, in response to receipt of the model input, output a model output;
• at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:
• determining a resource allocation parameter that corresponds to a desired allocation of system resources to the machine-learned model at an inference time;
• deactivating a subset of the plurality of blocks of the machine-learned model based on the resource allocation parameter;
• inputting the model input into the machine-learned model with the subset of the plurality of blocks deactivated;
• and
• receiving, as an output of the machine-learned model, the model output.