论文标题
Quadralib：架构优化和设计探索的表现二次神经网络库
QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration
论文作者
论文摘要
深层神经网络（DNN）的重大成功是由多个复杂的DNN库高度促进的。相反，尽管某些工作证明了二次深神经元网络（QDNNS）比一阶DNN表现出更好的非线性和学习能力，但其神经元设计却遭受了从理论性能到实际部署的某些缺点。在本文中，我们首先提出了一种新的QDNN神经元建筑设计，并进一步开发了QDNN库Quadralib，该库为QDNN提供了架构优化和设计探索。广泛的实验表明，我们的设计在多个学习任务上的预测准确性和计算消耗方面具有良好的性能。
The significant success of Deep Neural Networks (DNNs) is highly promoted by the multiple sophisticated DNN libraries. On the contrary, although some work have proved that Quadratic Deep Neuron Networks (QDNNs) show better non-linearity and learning capability than the first-order DNNs, their neuron design suffers certain drawbacks from theoretical performance to practical deployment. In this paper, we first proposed a new QDNN neuron architecture design, and further developed QuadraLib, a QDNN library to provide architecture optimization and design exploration for QDNNs. Extensive experiments show that our design has good performance regarding prediction accuracy and computation consumption on multiple learning tasks.
