RESOURCE BASED WORKLOAD ALLOCATION FOR MACHINE LEARNING WORKLOADS

Methods, systems, and devices for workload balancing for machine learning are described. Generally, a device may determine a size of a level one cache of a texture processor, identify a portion of input activation data for an iterative machine-learning process, and load the portion of input activation data into the level one cache. The device may allocate, based at least in part on a texture processor to shading processor arithmetic logic unit (ALU) resource ratio, a first set of one or more weight batches and a second set of one or more weight batches associated with the loaded portion of input activation data to the shading processor, and process the portion of input activation data based at least in part on the first set of one or more weight batches and the second set of one or more weight batches using the texture processor and the shading processor in parallel.