FAST DEEP NEURAL NETWORK FEATURE TRANSFORMATION VIA OPTIMIZED MEMORY BANDWIDTH UTILIZATION

摘要

Deep Neural Networks (DNNs) with many hidden layers and many units per layer are very flexible models with a very large number of parameters. As such, DNNs are challenging to optimize. To achieve real-time computation, embodiments disclosed herein enable fast DNN feature transformation via optimized memory bandwidth utilization. To optimize memory bandwidth utilization, a rate of accessing memory may be reduced based on a batch setting. A memory, corresponding to a selected given output neuron of a current layer of the DNN, may be updated with an incremental output value computed for the selected given output neuron as a function of input values of a selected few non-zero input neurons of a previous layer of the DNN in combination with weights between the selected few non-zero input neurons and the selected given output neuron, wherein a number of the selected few corresponds to the batch setting.

主权项

1. A method for improving computation time of speech recognition processing in an electronic device, the method comprising:
by a processor: updating a memory, corresponding to a selected given output neuron of a current layer of a Deep Neural Network (DNN), with an incremental output value computed for the selected given output neuron as a function of input values of a selected few non-zero input neurons of a previous layer of the DNN in combination with weights between the selected few non-zero input neurons and the selected given output neuron, wherein a number of the selected few corresponds to a batch setting; iterating the updating for each output neuron of the current layer; and repeating the updating and the iterating for each next selected few non-zero input neurons of the previous layer to reduce a rate of accessing the memory based on the batch setting to improve the computation time of the speech recognition processing.