  Previous works proved that the combination of the linear neuron network with
nonlinear activation functions (e.g. ReLu) can achieve nonlinear function
approximation. However, simply widening or deepening the network structure will
introduce some training problems. In this work, we are aiming to build a
comprehensive second-order CNN implementation framework that includes
neuron/network design and system deployment optimization.
