Abstract: A generative cooperative network (GCN) comprises a dataset generator model and a learner model. The dataset generator model generates training datasets used to train the learner model. The trained learner model is evaluated according to a reference training dataset. The dataset generator model is modified according to the evaluation. The training datasets, the dataset generator model, and the leaner model are stored by the GCN. The trained learner model is configured to receive input and to generate output based on the input.

Abstract: A system and method for evolving a recurrent neural network (RNN) that solves a provided problem includes: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes representing a neural network as a unique tree structure; an assembly module that assembles N RNN layers; an evolution module that evolves the H candidate RNN nodes of each respective RNN layer; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that evaluates a performance of each candidate RNN node of each RNN layer using validation data and assigns a fitness value to each candidate RNN node; a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.

Abstract: A generative cooperative network (GCN) comprises a dataset generator model and a learner model. The dataset generator model generates training datasets used to train the learner model. The trained learner model is evaluated according to a reference training dataset. The dataset generator model is modified according to the evaluation. The training datasets, the dataset generator model, and the leaner model are stored by the GCN. The trained learner model is configured to receive input and to generate output based on the input.

Abstract: A system and method for evolving a recurrent neural network (RNN) that solves a provided problem includes: a memory storing a candidate RNN genome database having a pool of candidate RNN nodes, each of the candidate RNN nodes representing a neural network as a unique tree structure; an assembly module that assembles N RNN layers; an evolution module that evolves the H candidate RNN nodes of each respective RNN layer; a training module that trains the candidate RNN nodes of each of the N RNN layers using training data; an evaluation module that evaluates a performance of each candidate RNN node of each RNN layer using validation data and assigns a fitness value to each candidate RNN node; a competition module that forms an elitist pool of candidate RNN nodes in dependence on their assigned fitness values; and a solution harvesting module providing for deployment of RNN layers instantiated with candidate RNN nodes from the elitist pool.

Abstract: A digital system is provided with a hierarchical memory system having at least a first and second level cache and a higher level memory. If a requested data item misses in both the first cache level and in the second cache level, a line of data containing the requested data is obtained from a higher level of the hierarchical memory system. The line of data is allocated to both the first cache level and to the second cache level simultaneously.

Abstract: A digital system is provided with a hierarchical memory system having at least a first and second level cache and a higher level memory. If a requested data item misses in both the first cache level and in the second cache level, a line of data containing the requested data is obtained from a higher level of the hierarchical memory system. The line of data is allocated to both the first cache level and to the second cache level simultaneously.