论文标题
卷积神经网络的最小过滤算法
Minimal Filtering Algorithms for Convolutional Neural Networks
论文作者
论文摘要
在本文中，我们介绍了几种有关在卷积神经网络的卷积层中执行的基本过滤操作的完全并行硬件实现的资源有效算法解决方案。实际上，这些基本操作计算了从当前数据流的滑动时间窗口形成的相邻向量的两个内部产物，并具有M-TAP有限脉冲响应过滤器的脉冲响应。我们使用了Winograd最小过滤技巧并将其应用于开发完全并行硬件的算法，以实现M = 3,5,7,9和11的基本过滤操作。在每种情况下，该算法的完全并行硬件实现实现了每种情况下的胚胎乘数与实现的胚胎数量相比，可提供大约30％的固定量，并提供了一个完全参与的硬件。
In this paper, we present several resource-efficient algorithmic solutions regarding the fully parallel hardware implementation of the basic filtering operation performed in the convolutional layers of convolution neural networks. In fact, these basic operations calculate two inner products of neighboring vectors formed by a sliding time window from the current data stream with an impulse response of the M-tap finite impulse response filter. We used Winograd minimal filtering trick and applied it to develop fully parallel hardware-oriented algorithms for implementing the basic filtering operation for M=3,5,7,9, and 11. A fully parallel hardware implementation of the proposed algorithms in each case gives approximately 30 percent savings in the number of embedded multipliers compared to a fully parallel hardware implementation of the naive calculation methods.
