Feature extraction. - Autonomous and Cyber-Physical Systems (ACPS) Research Group

PUDI DHILLESWARARAO, SRINIVAS BOPPU, M. SABARIMALAI MANIKANDAN, LINGA REDDY CENKERAMADDI, “Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey” has been accepted for publication in IEEE Access (2022).

Keywords: Field programmable gate arrays, Computer architecture, Deep learning, AI accelerators, Hardware acceleration, Graphics processing units, Feature extraction.

Abstract: In the modern-day era of technology, a paradigm shift has been witnessed in the areas involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Specifically, Deep Neural Networks (DNNs) have emerged as a popular field of interest in most AI applications such as computer vision, image and video processing, robotics, etc. In the context of developed digital technologies and the availability of authentic data and data handling infrastructure, DNNs have been a credible choice for solving more complex real-life problems. The performance and accuracy of a DNN is way better than human intelligence in certain situations. However, it is noteworthy that the DNN is computationally too cumbersome in terms of the resources and time to handle these computations. Furthermore, general-purpose architectures like CPUs have issues in handling such computationally intensive algorithms. Therefore, a lot of interest and efforts have been invested by the research fraternity in specialized hardware architectures such as Graphics Processing Unit (GPU), Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), and Coarse Grained Reconfigurable Array (CGRA) in the context of effective implementation of computationally intensive algorithms. This paper brings forward the various research works on the development and deployment of DNNs using the aforementioned specialized hardware architectures and embedded AI accelerators. The review discusses the detailed description of the specialized hardware-based accelerators used in the training and/or inference of DNN. A comparative study based on factors like power, area, and throughput, is also made on the various accelerators discussed. Finally, future research and development directions, such as future trends in DNN implementation on specialized hardware accelerators, are discussed. This review article is intended to guide hardware architects to accelerate and improve the effectiveness of deep learning research.

More details: DOI: 10.1109/ACCESS.2022.3229767

Kenneth Bremnes, Rebecca Moen, Sreenivasa Reddy Yeduri, Rakesh Reddy Yakkati, and Linga Reddy Cenkeramaddi, “Classification of UAVs utilizing Fixed Boundary Empirical Wavelet Subbands of RF Fingerprints and Deep Convolutional Neural Network,” has been accepted for publication in IEEE Sensors Journal (2022).

Keywords: RF signals, Drones, Fingerprint recognition, Convolutional neural networks, Autonomous aerial vehicles, Radio frequency, Feature extraction

Abstract: Unmanned aerial vehicle (UAV) classification and identification have many applications in a variety of fields, including UAV tracking systems, antidrone systems, intrusion detection systems, military, space research, product delivery, agriculture, search and rescue, and internet carrier. It is challenging to identify a specific drone and/or type in critical scenarios, such as intrusion. In this article, a UAV classification method that utilizes fixed boundary empirical wavelet sub-bands of radio frequency (RF) fingerprints and a deep convolutional neural network (CNN) is proposed. In the proposed method, RF fingerprints collected from UAV receivers are decomposed into 16 fixed boundary empirical wavelet sub-band signals. Then, these sub-band signals are then fed into a lightweight deep CNN model to classify various types of UAVs. Using the proposed method, we classify a total of 15 different commercially available UAVs with an average testing accuracy of 97.25%. The proposed model is also tested with various sampling points in the signal. Furthermore, the proposed method is compared with recently reported works for classifying UAVs utilizing remote controller RF signals.

More details:DOI: 10.1109/JSEN.2022.3208518

Tag: Feature extraction.

The article entitled, “Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey” has been accepted for publication in IEEE Access (2022).

The article titled, “Classification of UAVs utilizing Fixed Boundary Empirical Wavelet Subbands of RF Fingerprints and Deep Convolutional Neural Network,” has been accepted for publication in IEEE Sensors Journal (2022).