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.

中文翻译：

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二次神经元的高效神经网络实现

先前的工作证明线性神经元网络与非线性激活函数（例如ReLu）的组合可以实现非线性函数逼近。但是，简单地拓宽或深化网络结构会带来一些培训问题。在这项工作中，我们旨在建立一个全面的二阶CNN实施框架，其中包括神经元/网络设计和系统部署优化。