Kun He
ABSTRACT
Modulation recognition is one of the crucial tasks in intelligent communications. With the development of deep learning, modulation recognition based on deep neural networks has attracted significant attention. Meanwhile, with development of internet of things as well as edge computing, various embedded devices have emerged. Consequently, how to deploy the deep neural network of modulation recognition on embedded devices becomes a research hotspot. Existing inference frameworks for the deep neural network of modulation recognition are highly dependent on the hardware platform, suffer from weak universality, and cannot be widely transplanted into various embedded devices. To solve this problem, this paper proposes a general inference framework for the modulation recognition network. The framework is built with the standard C language library, which is generally supported by embedded devices, to construct all the operators in the deep neural network, so as to ensure that the deployment of the framework is not limited by the hardware platform. Test results show that the inference framework proposed in this paper can run well on various embedded devices and achieve modulation recognition without accuracy loss.
- S. Banerjee, A. Hati, S. Chaudhuri, and R. Velmurugan. 2018. Image Co-segmentation using Graph Convolution Neural Network. In Indian Conf. on Vision, Graphics and Image Processing (ICVGIP).Google Scholar
- Théo Benoit-Cattin, Delia Velasco-Montero, and Jorge Fernández-Berni. 2020. Impact of thermal throttling on long-term visual inference in a CPU-based edge device. Electronics 9, 12 (2020), 2106.Google ScholarCross Ref
- Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830(2016).Google Scholar
- Deep Compression Han, Huizi Mao, and WJ Dally Deep Compression. 2016. Compressing Deep Neural Networks with Pruning. Trained Quantization and Huffman Coding, arXiv (2016).Google Scholar
- X. He, W. Lu, G. Yan, and Z. Xuan. 2018. Joint Design of Training and Hardware Towards Efficient and Accuracy-Scalable Neural Network Inference. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8 (2018), 810–821.Google ScholarCross Ref
- Meng-Ju Hsieh, Shih-Chang Hsia, and Szu-Hong Wang. 2021. Chip Design of Convolution Computation for AI Network. In 2021 IEEE 4th International Conference on Knowledge Innovation and Invention (ICKII). IEEE, 49–53.Google Scholar
- C. Juvekar, V. Vaikuntanathan, and A. Chandrakasan. 2018. Gazelle: A Low Latency Framework for Secure Neural Network Inference.Google Scholar
- A. Karpathy. 2015. The Unreasonable Effectiveness of Recurrent Neural Networks. (2015).Google Scholar
- Tussanai Parthornratt, Natchaphon Burapanonte, and Wisarute Gunjarueg. 2016. People identification and counting system using raspberry Pi (AU-PiCC: Raspberry Pi customer counter). In 2016 International Conference on Electronics, Information, and Communications (ICEIC). IEEE, 1–5.Google ScholarCross Ref
- S. Peng, H. Jiang, H. Wang, H. Alwageed, and Y. D. Yao. 2017. Modulation classification using convolutional Neural Network based deep learning model. In 2017 26th Wireless and Optical Communication Conference (WOCC).Google ScholarCross Ref
- Shengliang Peng, Hanyu Jiang, Huaxia Wang, Hathal Alwageed, Yu Zhou, Marjan Mazrouei Sebdani, and Yu-Dong Yao. 2018. Modulation classification based on signal constellation diagrams and deep learning. IEEE transactions on neural networks and learning systems 30, 3(2018), 718–727.Google Scholar
- Shengliang Peng, Shujun Sun, and Yu Dong Yao. 2021. A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing. IEEE Transactions on Neural Networks and Learning Systems PP, 99(2021), 1–19.Google Scholar
- Zhongnan Qu, Zimu Zhou, Yun Cheng, and Lothar Thiele. 2020. Adaptive loss-aware quantization for multi-bit networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7988–7997.Google ScholarCross Ref
- Ching-Lung Su, Wen-Cheng Lai, Yu-Kai Zhang, Ting-Jia Guo, Yi-Jiun Hung, and Hui-Chiao Chen. 2020. Artificial Intelligence Design on Embedded Board with Edge Computing for Vehicle Applications. In 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE, 130–133.Google Scholar
- Yishan Su, Lijie Dong, Zhaojia Zhou, Xuan Liu, and Xing Wei. 2020. A General Embedded Underwater Acoustic Communication System Based on Advance STM32. IEEE Embedded Systems Letters 13, 3 (2020), 90–93.Google ScholarDigital Library
- Han Vanholder. 2016. Efficient inference with tensorrt. In GPU Technology Conference, Vol. 1. 2.Google Scholar
- Vincent Vanhoucke, Andrew Senior, and Mark Z Mao. 2011. Improving the speed of neural networks on CPUs. (2011).Google Scholar
- J. Wu, L. Cong, Y. Wang, Q. Hu, and C. Jian. 2016. Quantized Convolutional Neural Networks for Mobile Devices. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Shenghao XU, Wei QUAN, and Huang Shukang. [n. d.]. Ncnn-YOLOv3 Acceleration and Implementation. ([n. d.]).Google Scholar
Index Terms
- A General Inference Framework for Deep Neural Network of Modulation Recognition
Recommendations
Research on the Modulation Recognition Method OFDM Signal Based on Short-time Fourier Transform and Convolutional Neural Network
ICBTA '21: Proceedings of the 2021 4th International Conference on Blockchain Technology and ApplicationsIn order to improve the modulation recognition accuracy of the Orthogonal Frequency Division Multiplexing (OFDM) signal under low signal-to-noise ratio, an OFDM signal modulation recognition method based on short-time Fourier transform and convolutional ...
Modulation Recognition Based on Denoising Bidirectional Recurrent Neural Network
AbstractModulation recognition is an important research area in wireless communication. It is commonly used in both military and civilian domains, such as spectrum detection and interference identification. Most existing modulation recognition algorithms ...
Extended deep neural network for facial emotion recognition
Highlights- A new Deep Fully Connected model for facial emotion recognition.
- The model ...
Comments