Computational Complexity Of Convolutional Neural Networks Research Articles

Convolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices

Vehicle classification has an important role in the efficient implementation of Internet of Things (IoT)-based intelligent transportation system (ITS) applications. Nowadays, because of their higher performance, convolutional neural networks (CNNs) are mostly used for vehicle classification. However, the computational complexity of CNNs and high-resolution data provided by high-quality monitoring cameras can pose significant challenges due to limited IoT device resources. In order to address this issue, this study aims to propose a simple CNN-based model for vehicle classification in low-quality images collected by a standard security camera positioned far from a traffic scene under low lighting and different weather conditions. For this purpose, firstly, a new dataset that contains 4800 low-quality vehicle images with 100 × 100 pixels and a 96 dpi resolution was created. Then, the proposed model and several well-known CNN-based models were tested on the created dataset. The results demonstrate that the proposed model achieved 95.8% accuracy, outperforming Inception v3, Inception-ResNet v2, Xception, and VGG19. While DenseNet121 and ResNet50 achieved better accuracy, their complexity in terms of higher trainable parameters, layers, and training times might be a significant concern in practice. In this context, the results suggest that the proposed model could be a feasible option for IoT devices used in ITS applications due to its simple architecture.

Real-Time Facial Affective Computing on Mobile Devices.

Convolutional Neural Networks (CNNs) have become one of the state-of-the-art 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 real-time facial affective computing tasks. We propose a light-weight 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 state-of-the-art methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a real-time facial affective computing application on an actual mobile device.

BitStream: An efficient framework for inference of binary neural networks on CPUs

Convolutional Neural Networks (CNN) has been well-studied and widely used in the field of pattern recognition. Many pattern recognition algorithms need features extracted from CNN models to adapt to complex tasks, such as image classification, object detection, natural language processing and so on. However, to deal with more and more complex tasks, modern CNN models are becoming larger and larger, contain large number of parameters and computation, leading to high consumption of memory, computational and power resources during inference. This makes it difficult to run CNN based applications in real time on mobile devices, where memory, computational and power resources are limited. Binarization of neural networks is proposed to reduce memory and computational complexity of CNN. However, traditional implementations of Binary Neural Networks (BNN) follow the conventional im2col-based convolution computation flow, which is widely used in floating-point networks but not friendly enough to cache when it comes to binarized neural networks. In this paper, we propose BitStream, a general architecture for efficient inference of BNN on CPUs. In BitStream, we propose a simple but novel computation flow for BNN. Unlike existing implementations of BNN, in BitStream, all the layers, including convolutional layers, binarization layers and pooling layers are all calculated in binary precision. Comprehensive analyses demonstrate that our proposed computation flow consumes less memory during inference of BNN, and it’s friendly to cache because of its continuous memory access.