8 citations · 9 across the 4 of their papers we have counts for
4 papers
Enabling Memory-efficient Im2win Convolution with Multi-precision Support on GPU CUDA and Tensor Cores
Xiang Fu, Jixiang Ma, Xinpeng Zhang +3
Convolution is a principal computational bottleneck in deep neural networks, and its efficiency depends on tight integration between algorithms and GPU hardware. Existing GPU convo…
High Performance Im2win and Direct Convolutions using Three Tensor Layouts on SIMD Architectures
Xiang Fu, Xinpeng Zhang, Jixiang Ma +3
Convolution is the core component within deep neural networks and it is computationally intensive and time consuming. Tensor data layouts significantly impact convolution operation…
Im2win: Memory Efficient Convolution On SIMD Architectures
Shuai Lu, Jun Chu, Xu T. Liu
Convolution is the most expensive operation among neural network operations, thus its performance is critical to the overall performance of neural networks. Commonly used convoluti…
Im2win: An Efficient Convolution Paradigm on GPU
Shuai Lu, Jun Chu, Luanzheng Guo +1
Convolution is the most time-consuming operation in deep neural network operations, so its performance is critical to the overall performance of the neural network. The commonly us…