Abstract: Extensive training of DNNs may take a significant amount of time due to redundancy in data processing within network nodes. Improvement may be made by a method for training a Deep Neural Network (DNN) ensemble, include the steps of: executing by at least a processor in a computer, program code of a compiler which is stored in a non-transitory computer-readable medium, wherein the compiler configures a Deep Neural Network (DNN) into N networks to perform training steps: (a) receiving, a plurality of inputs i . . . I, by a plurality of neurons ni . . . nx, wherein each neuron ni being a computation node comprised in the N networks of the DNN; (b) utilizing by the compiler, the plurality of inputs i . . . I to train to the N networks to ensemble the DNN through analyzing, identifying and removing inter-network neuron redundancy to obtain savings in training time constraints and a reduction in original memory footprint.

Abstract: Methods and apparatus for parallel processing are provided. A multicore processor is described. The multicore processor may include a distributed memory unit with memory nodes coupled to the processor's cores. The cores may be configured to execute parallel threads, and at least one of the threads may be data-dependent on at least one of the other threads. The distributed memory unit may be configured to proactively send shared memory data from a thread that produces the shared memory data to one or more of the threads.

Abstract: Methods and apparatus for parallel processing are provided. A multicore processor is described. The multicore processor may include a distributed memory unit with memory nodes coupled to the processor's cores. The cores may be configured to execute parallel threads, and at least one of the threads may be data-dependent on at least one of the other threads. The distributed memory unit may be configured to proactively send shared memory data from a thread that produces the shared memory data to one or more of the threads.