  We present a new methodology for improving the accuracy of small neural
networks by applying the concept of a clos network to achieve maximum
expression in a smaller network. We explore the design space to show that more
layers is beneficial, given the same number of parameters. We also present
findings on how the relu nonlinearity ffects accuracy in separable networks. We
present results on early work with Cifar-10 dataset.
