Oct 15, 2020 · In this paper, we aim to improve the performance, time complexity and energy efficiency of deep convolutional neural networks (CNNs) by combining hardware and ...
In this paper, we aim to improve the performance, time complexity and energy efficiency of deep convolutional neural networks (CNNs) by combining hardware ...
Oct 15, 2020 · In this paper, we aim to improve the performance, time complexity and energy efficiency of deep convolutional neural networks (CNNs) by ...
In this paper, we aim to improve the performance, time complexity and energy efficiency of deep convolutional neural networks (CNNs) by combining hardware ...
Abstract: In this paper, we aim to improve the performance, time complexity and energy efficiency of deep convolutional neural networks (CNNs) by combining ...
... Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks. Neural Computing and Applications, 2021, 33 (12), pp.6443-6465 ...
Since the pooling step represents a process that contributes significantly to CNNs performance improvement, we propose the Mode-Fisher pooling method. This form ...
The Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks ... A novel proposed pooling for convolutional neural network.
The Mode-Fisher (MF) pooling method is proposed, which reduces significantly the data movement in the CNN and save up to 10% of total energy, ...
The Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks · Author Picture Dou El Kefel Mansouri,; Author Picture Bachir ...