Abstract: The disclosed systems and methods for intelligent heterogeneous computation directed to receiving monitoring data and a set of training data, wherein the monitoring data includes an occupancy rate of a preprocessed data queue and a utilization factor of accelerating devices, generating a resource computation job list in accordance with the monitoring data, forwarding jobs, in the resource computation job list to be executed on a central processing unit (CPU), to a CPU worker queue, forwarding control messages to the CPU worker queue, wherein the control messages are associated with jobs in the resource computation job list to be executed on the accelerating devices, and executing, by the accelerating devices, jobs in the resource computation job list to be executed on the accelerating devices.

Abstract: The disclosed systems and methods are directed to generating cache IDs for each of a plurality of AI training pipelines, accessing training data elements included in a training data set, generating IDs corresponding to the training data elements, receiving the data IDs and an associated cache ID, randomizing the data IDs, selecting a subset of the randomized data IDs, fetching the training data elements previously cached by a cache node, receiving a portion of the training data elements present in the caching server corresponding to the subset of randomized data IDs, forwarding the portion of the training data elements present in the caching server to at least one consumer node, fetching the remaining training data elements associated with the subset of randomized data IDs from the training data set, and forwarding the remaining training data elements to at least one transformation node for training the neural network.