Citation 'Dynamic Spatial-wise Convolutional Block with Fused Activation Functions and Batch Normalization for Low-Latency Neural Network Acceleration', Zhou, Li, et al. https://arxiv.org/abs/1608.01774 'Dynamic Spatial-wise Convolutional Block with Fused Activation Functions and Batch Normalization for Low-Latency Neural Network Acceleration' 2019. In Proceedings of the 2019 International Conference on Computer Vision (ICCV), Sydney, Australia, 12-17 October, 2019.
The structural configuration of a convolutional block is composed of the number of input and output channels, the number of output feature maps, the kernel size and its stride. The activation rate is the average number of activations generated by the convolutional block on each input. We use a 3-layer fully connected network to learn the latency prediction model and a linear regression model to estimate the latency of the operator. We also add noise to the final network output to improve the generalization capability. The latency prediction model is trained in an end-to-end manner, and applied to the operator to produce the latency estimate.
We use general optimization methods like fusing activation functions and batch normalization layers into convolution layers.We also optimize the specific operators in our spatial-wise dynamic convolutional blocks as follows (see also Figure
7) If we apply a activation function, such as ReLU, the parameters of the activation function are updated. The updated parameters of the base convolutional layer, the final output, and the parameters of the activation function are saved and reused in the next step.
As the real latency is not publicly known, we make use of an online server to estimate the latency of the above-mentioned operators. Meanwhile, we also use the above-mentioned latency estimation model to predict the latency of the operators, so that the latency of the whole network can be estimated as well as the parameter and activation functions to be used in the following layers.
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