FunctionallyPredefined Kernel: a Way to Reduce CNN Computation Convolutional Neural Networks (CNNs) have achieved high classification accuracy in image recognition, and now, they are widely used for numerous applications. For higher accuracy or more advanced applications, CNNs need to consume tremendous computational resources and time. Hence, many studies for reducing the computational cost of CNNs are actively being conducted. However, many previous methods for reducing the computational cost lead to a nonnegligible loss in output accuracy. Therefore, it is still a challenge to reduce the computational cost of CNNs with keeping the output accuracy high. In this paper, we propose a novel concept FunctionallyPredefined Kernel to reduce the computational cost for CNN training and discuss the potential of computation reuse to reduce the computational cost for CNN inference. Our experimental results show that the number of parameters to be trained can be significantly reduced by utilizing FunctionallyPredefined Kernels without accuracy loss. In addition, we revealed that CNNxe2x80x99s inference process includes many convolution operations with the same inputs and computation reuse, therefore, has high affinity to CNN computation.

RealTime Facial Affective Computing on Mobile Devices Convolutional Neural Networks (CNNs) have become one of the stateoftheart methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for realtime facial affective computing tasks. We propose a lightweight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with stateoftheart methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a realtime facial affective computing application on an actual mobile device.