In response to the high computational demands of 3D Convolutional Neural Networks(CNNs)in environments with limited resources,this paper proposes an optimization approach that synergizes the Winograd algorithm with network pruning techniques.Firstly,the method replaces conventional 3D convolution layers with more efficient 3D Winograd layers to expedite convolution processes.Secondly,it involves assessing the importance of weights within the 3D Winograd layers,which facilitates the formation of a sparse model via pruning.Finally,the sparse model is retrained to further recover network performance.This combined use of the Winograd algorithm for computational efficiency and network pruning for model simplification results in a substantial reduction in computational needs while maintaining or improving the ac-curacy of recognition.Comparative experimental results affirm the proposed approach signifi-cantly minimizes computational resource use,maintaining or even boosting recognition capabili-ties compared to other optimization methods.
关键词
3D卷积神经网络优化/Winograd算法/网络剪枝
Key words
Optimization of 3D CNNs/Winograd Algorithm/Network Pruning
维普
万方数据