METHODS AND APPARATUS TO FACILITATE TILE-BASED GPU MACHINE LEARNING ACCELERATION

The present disclosure relates to methods and apparatus for machine learning processing. For example, disclosed techniques facilitate tile-based GPU machine learning acceleration. Aspects of the present disclosure can determine a tile size based on a memory size of a first memory and a job input size associated with executing a computational job. In some examples, the computational job may be one of a quantity of computational jobs configured to execute a machine learning primitive. Aspects of the present disclosure can also load, based on the tile size, input data associated with a batch of computational jobs from a second memory to the first memory. Further, aspects of the present disclosure can generate batch output data by executing the batch of computational jobs using the input data loaded to the first memory. Additionally, aspects of the present disclosure can store the generated batch output data to the second memory.