论文标题
优化技术以提高FPGA上正向传播神经网络的推理性能
Optimization Techniques to Improve Inference Performance of a Forward Propagating Neural Network on an FPGA
论文作者
论文摘要
本文介绍了以前曾经训练过的前向传播分类神经网络的优化实现。该实施描述的是，强调了使用Python脚本生成Verilog硬件实现的一种新颖手段。此实现的特征包括优化缩放输入数据，使用选定的加成代替乘法功能，硬件友好的激活功能和简化的输出选择。将详细详细介绍Intel I7与Xilinx FPGA的软件实现之间的28x28像素“手写”识别的推理性能比较。
This paper describes an optimized implementation of a Forward Propagating Classification Neural Network which has been previously trained. The implementation described highlights a novel means of using Python scripts to generate a Verilog hardware implementation. The characteristics of this implementation include optimizations to scale input data, use selected addends instead of multiplication functions, hardware friendly activation functions and simplified output selection. Inference performance comparison of a 28x28 pixel 'hand-written' recognition NN between a software implementation on an Intel i7 vs a Xilinx FPGA will be detailed.
