卷积神经网络的最小滤波算法
Minimal Filtering Algorithms for Convolutional Neural Networks
摘要：
在本文中，我们提出了几个资源高效的算法解决方案，关于在卷积神经网络的卷积层中执行的基本滤波操作的完全并行硬件实现。实际上，这些基本操作计算由滑动时间窗从当前数据流形成的相邻向量与M抽头有限脉冲响应滤波器的脉冲响应的两个内积。我们使用Winograd最小滤波技巧，并将其应用于开发完全并行的面向硬件的算法，用于实现M= 3，5，7，9和11的基本滤波操作。在每种情况下，所提出的算法的完全并行硬件实现给出了大约30%的节省嵌入式乘法器的数量相比，一个完全并行的硬件实现的天真的计算方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
