标题： 快速 Clifford 神经层 显示英文标题

标题： Fast Clifford Neural Layers

摘要： 克利福德神经层通过将克利福德代数引入神经网络来改进PDE建模。 在这个项目中，我们专注于优化单个核心CPU性能的2/3D克利福德卷积层和多重向量激活层的推理。 总体而言，通过在一个涉及克利福德卷积层和多重向量激活层的真实网络块上进行测试，我们观察到我们的实现比标准的PyTorch实现在相对较大的数据+网络规模（>L2缓存）情况下快30%。 我们在https://github.com/egretwAlker/c-opt-clifford-layers开源了我们的代码库。 显示英文摘要

摘要： Clifford Neural Layers improve PDE modeling by introducing Clifford Algebra into neural networks. In this project we focus on optimizing the inference of 2/3D Clifford convolutional layers and multivector activation layers for one core CPU performance. Overall, by testing on a real network block involving Clifford convolutional layers and multivector activation layers, we observe that our implementation is 30% faster than standard PyTorch implementation in relatively large data + network size (>L2 cache). We open source our code base at https://github.com/egretwAlker/c-opt-clifford-layers