Fast ConvNets Using Group-wise Brain Damage

We revisit the idea of brain damage, i.e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers. The approach uses the fact that many efficient implementations reduce generalized convolutions to matrix multiplications. The suggested brain damage process prunes the convolutional kernel tensor in a group-wise fashion by adding group-sparsity regularization to the standard training process. After such group-wise pruning, convolutions can be reduced to multiplications of thinned dense matrices, which leads to speedup. In the comparison on AlexNet, the method achieves very competitive performance

Analysis of the state of information security on the basis of surious emission electronic components

The article deals with an approach to determining the state of information security based on the analysis of spurious emission of electromagnetic components. Attention is drawn to the possibility of the formation of the data, obtain samples for the analysis of the state of the information security. An experiment in result of which the amplitude-frequency characteristics of the analyzed radiation. Formed data tuples estimated probability values correctly determine the state on the basis of the data obtained