Scheduling configuration for deep learning networks

An electronic device, comprising:

• a cluster of interconnected graphics processors, each graphics processor including a plurality of processing resources comprising at least a first type of processing resource and a second type of processing resource, different from the first type of processing resource, the graphics processors of the cluster of interconnected graphics processors interconnected via a first plurality of point-to-point interconnects, at least a portion of the graphics processors of the cluster of interconnected graphics processors additionally interconnected via a second plurality of interconnects to a host interface switch;
• and
• a processor, separate from the plurality of processing resources, to schedule operations to the cluster of interconnected graphics processors, the processor configured to:
• determine a traversal strategy for a deep learning neural network comprising an input layer and an output layer separated by at least one hidden layer which transforms an input received by the input layer into a representation useful for generating an output in the output layer, the traversal strategy to be implemented via dispatch components of the graphics processors in the cluster of interconnected graphics processors;
• convey the traversal strategy to the dispatch components of the graphics processors, the traversal strategy for a three-dimensional (3D) object divided into 3D tiles, each tile represented as a 3D cube in memory of the graphics processors, the graphics processors configured to;
• receive the traversal strategy and data for the deep learning neural network;
• traverse a solution space of the deep learning neural network to score a plurality of solutions to schedule deep learning network execution on the plurality of processing resources of a graphics processor;
• select a solution from the plurality of solutions to implement the deep learning network based on scores associated with the plurality of solutions;
• and
• implement a workload schedule to assign tasks to the plurality of processing resources, wherein the workload schedule specifies a batch of grouped operations, the operations of the batch of grouped operations determined via a machine learning model based on historical data associated with the cluster of interconnected graphics processors.