Efficient Neural Network Implementation with Quadratic Neuron

Abstract

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.

2 pages, 1 figure, accepted by 2020 3rd IBM IEEE CAS/EDS AI Compute Symposium