Abstract: A neural network structure is separated into an odd neural network including only the odd layers and an even neural network including only the even layers. In order to allow for parallel execution, for forward propagation a second input is generated from the original input, while for backward propagation a second error gradient is generated. Parallel execution may accelerate the forward and backward propagation operations without significant change in accuracy of the model. Additionally, restructuring a single neural network into two or more parallel neural networks may reduce the total time needed for training.

Abstract: A neural network structure is separated into an odd neural network including only the odd layers and an even neural network including only the even layers. In order to allow for parallel execution, for forward propagation a second input is generated from the original input, while for backward propagation a second error gradient is generated. Parallel execution may accelerate the forward and backward propagation operations without significant change in accuracy of the model. Additionally, restructuring a single neural network into two or more parallel neural networks may reduce the total time needed for training.