Algorithm

Linguistics

Philosophy

Layer (electronics)

Operating system

Computation

Parallel computing

Computer science

FLOPS

Organic chemistry

Pattern recognition (psychology)

Macro

Feature (linguistics)

Computer engineering

Artificial intelligence

Chemistry

Programming language

Deep learning

Artificial neural network

Mobile device

Convolutional neural network

Convolution (computer science)

Deep neural networks perform better than traditional machine learning methods on various classification problems by producing good quality feature maps through successive convolution operation(s). However, when implementing a deep neural network in an embedded system or SoC for mobile devices, its large parameter size can be a significant burden on the internal memory design. In this paper, we propose a new deep neural network that reduces computation and the number of model parameters but maintains reasonable performance. The configuration of the proposed network is as follows: First, we propose a macro unit (MU) to reduce heavy computations and to learn various feature maps. Second, we employ asymmetric convolution of the well-known Inception network to further efficiently manipulate feature maps within the MU. Third, all the feature maps produced from MU(s) of each layer are concatenated and then the grouped feature map is distributed to all the MUs of the next layer for transferring richer information. Experimental results show that the proposed network achieves about 10% higher performance than DenseNet-BC in case of extremely small parameter size for CIFAR-100. The proposed network also has very few learning parameters and smaller floating point operations per second (FLOPS) than the other networks optimized for mobile devices such as MobileNet.

MUNet: Macro Unit-Based Convolutional Neural Network for Mobile Devices